Ralitsa Golemanova

Beyond Human Vision: The Evolution of Image Recognition Accuracy

Technologies based on Artificial Intelligence are all the rage these days — both because of their stunning capabilities and the numerous ways in which they make our lives easier and because of the unknown future that we project they may bring. 

One particular stream of development in the field of AI is image recognition based on machine learning algorithms. It’s being used in so many fields today that it’s challenging to start counting them. 

In the fast-paced digital world, image recognition powers up crucial activities like content moderation on a large scale, as required by the exponentially growing volume of user-generated content on social platforms. 

It’s not only that, though: image recognition finds great uses in construction, security, healthcare, e-commerce, entertainment, and numerous other fields where it brings unseen benefits in terms of productivity and precision. Think also about innovations like self-driving cars, robots, and many more — all made possible by computer vision. 

But how did image recognition start, and how did it evolve over the decades to reach the current levels of broad use and accuracy that sometimes even surpass the capabilities of human vision? 

It all started with a scientific paper by two neurophysiologists in the distant 1959, which was dealing with research on cat’s neurons… 

Let’s dive into the history of this field of AI-powered technology in the sections below. 

1950s: The First Seeds of the Image Recognition Scientific Discipline

As with many other human discoveries, image recognition started out as a research interest in different other fields. 

In the last years of the 1950s, two important events occurred that were far away from the creation and use of computer systems but were central to developing the concept of image recognition. 

In 1959, the neurophysiologists David Hubel and Torsten Wiesel published their research on the Receptive Fields of Single Neurons in the Cat's Striate Cortex. The paper became popular and widely recognized, as the two scientists made an important discovery while studying the responses of visual neurons in cats and in particular, how their cortical architecture is shaped. 

Hubel and Wiesel found that the primary visual cortex has simple and complex neurons. They also discovered that the process of recognizing an image begins with the identification of simple structures, such as the edges of the items being seen. Afterward, the details are added, and the whole complex image is understood by the brain. Their research on cats thus, by chance, became a founding base for image recognition based on computer technologies. 

The second important event from the same year was the development of the first technology for the digital scanning of images. Russel Kirsch and a group of researchers led by him invented a scanner that could transform images into numbers so that computers can process them. This historical moment led to our current ability to handle digital images for so many different uses. 

1960s and 1970s: Image Recognition Becomes an Official Academic Discipline

The 1960s were the time when image recognition was officially founded. Artificial intelligence, and hence image recognition as a significant part of it, was recognized as an academic discipline with growing interest from the scientific community. Scientists started working on the seemingly wild idea of making computers identify and process visual data. These were the years of dreams about what AI could do — and the projections for revolutionary advancements were highly positive. 

The name of the scientist Lawrence Roberts is linked to the creation of the first image recognition or computer vision applications. He put the start of it all by publishing his doctoral thesis on Machine Perception of Three-Dimensional Solids. In it, he details how 3D data about objects can be obtained from standard photos. Roberts’ first goal was to convert photos into line sketches that could then become the basis for 3D versions. His thesis presented the process of turning 2D into 3D representations and vice versa. 

Roberts’ work became the ground for further research and innovations in 3D creation and image recognition. They were based on the processes of identifying edges, noting lines, construing objects as consisting of smaller structures, and the like, and later on included contour models, scale-space, and shape identification that accounts for shading, texture, and more.  

Another important name was that of Seymour Papert, who worked at the AI lab at MIT. In 1966, he created and ran an image recognition project called “Summer Vision Project.” Papert worked with MIT students to create a platform that had to extract the background and front parts of images, as well as to detect objects that were not overlapping with others. They connected a camera to a computer to mimic how our brains and eyes work together to see and process visual information. The computer had to imitate this process of seeing and noting the recognized objects — thus, computer vision came to the front. Regretfully, the project wasn’t deemed successful, but it is still recognized as the first attempt at computer-based vision within the scientific realm. 

1980s and 1990s: The Moves to Hierarchical Perception and Neural Networks 

The next big moment in the evolution of image recognition came in the 1980s. In the following two decades, the significant milestones included the idea of hierarchical processing of visual data, as well as the founding blocks of what later came to be known as neural networks.  

The British neuroscientist David Marr presented his research "Vision: A computational investigation into the human representation and processing of visual information" in 1982. It was founded on the idea that image recognition’s starting point is not holistic objects. Instead, he focused on corners, edges, curves, and other basic details as the starting points for deeper visual processing. 

According to Marr, the image processing had to function in a hierarchical manner. His approach stated that simple conical forms can be employed to put together other complex objects. 

The evolution of the Hough Transform, a method for recognizing complex patterns, was another important event around this period. The algorithm was foundational for creating advanced image recognition methods like edge identification and feature extraction. 

At the beginning of the 1980s, another significant step forward in the image recognition field was made by the Japanese scientist Kunihiko Fukushima. He invented the Neocognitron, seen as the first neural network categorized as ‘deep’. It is believed to be the predecessor of the present-day convolutional networks used in machine learning-based image recognition. 

The Neocognitron artificial network consisted of simple and complex cells that identified patterns irrespective of position shifts. It was made up of a number of convolutional layers, each triggering actions that served as input for the next layers. 

In the 1990s, there was a clear shift away from David Marr’s ideas about 3D objects. AI scientists focused on the area of recognizing features of objects. David Lowe published the paper Object Recognition from Local Scale-Invariant Features in 1999, which detailed an image recognition system that employs features that are not subject to changes from location, light, and rotation. Lowe saw a resemblance between neurons in the inferior temporal cortex and these features of the system.

Gradually, the idea of neural networks came to the front. It was based on the structure and function of the human brain — with the idea of teaching computers to learn and spot patterns. This is how the first convolutional neural networks (CNNs) came about, equipped to gather complex features and patterns for more complicated image recognition tasks. 

Again, in the 1990s, the interplay between computer graphics and computer vision pushed the field forward. Innovations like image-based rendering, morphing, and panorama stitching brought about new ways to think about where image recognition could go. 

2000s and 2010s: The Stage of Maturing and Mass Use

In the first years of the 21st century, the field of image recognition reshifted towards object recognition as a primary goal. The first two decades were a time of steady growth and breakthroughs that eventually led to the mass adoption of image recognition in different types of systems. 

In 2006, Princeton Alumni Fei-Fei Lin, who later became a Professor of Computer Science at Stanford, was conducting machine learning research and was facing the challenges of overfitting and underfitting. To address them, in 2007, she founded Imagenet, an ameliorated dataset that could power machines to make more accurate judgments. In 2010, the dataset consisted of three million visual items, tagged and categorized in over 5,000 sections. Imagenet served as a major milestone for object recognition as a whole. 

In 2010, the first Imagenet Large Scale Visual Recognition Challenge (ILSVRC) brought about the massive evaluation of object identification and classification algorithms. 

It led to another significant step in 2012 — Alexnet. The scientist Alex Krizhevsky was behind this project, which employed architecture based on convolutional neural networks. Alexnet was recognized as the first use of deep learning. This brought about a significant reduction in error rates and boosted the whole field of image recognition. 

All in all, the progress with Imagenet and its subsequent initiatives was revolutionary, and the neural networks set up back then are still being used in various applications, such as the popular photo tagging on social networks. 

2020s: The Power of Image Recognition Today

Our current decade is a witness to a powerful move in image recognition to maximize the potential of neural networks and deep learning algorithms. With their help, deep learning algorithms are constantly evolving and gaining higher levels of accuracy, as well as pushing further the advancement of the whole field with a focus on classification, segmentation, and optical flow, among others.  

The industries and applications in which image recognition is being used today are innumerable. Just a few of them include content moderation on digital platforms, quality inspection and control in manufacturing, project and asset management in construction, diagnostics and other technological advancements in healthcare, automation in areas like security and administration, and many more. 

Learn How Image Recognition Can Boost Your Business

At Imagga, we are committed to the most forward-looking methods in developing image recognition technologies — and especially tailor-made solutions such as custom categorization — for businesses in a wide array of fields. 

Do you need image tagging, facial recognition, or a custom-trained model for image classification? Get in touch to see how our solutions can power up your business. 

by Ralitsa Golemanova

Trust and Safety Requirements for Better Content Moderation

The rise of digital communities and online marketplaces has brought immense changes in the ways we interact with each other, purchase goods, and seek various professional services. 

On one hand, all the different social platforms with user-generated content allow us to communicate with peers and larger social circles and share our experiences through text, audio and visuals. This has expanded and moulded our social lives dramatically. 

At the same time, the digital world has taken over the ways in which we look for and buy products and services. Many of us have embraced online shopping, as well as the sharing economy — from cab rides to apartments. 

While many of these advancements are undoubtedly making our lives easier and often more enjoyable, the shift to digital brings about some challenges too. Among the most significant ones is ensuring the safety of online users. Protecting people from fraud, scam and misuse, inappropriate and harmful content, and other types of digital risks has thus become essential for digital platforms of different kinds. 

The mass adoption and success of a social community or marketplace today is directly linked to the level of trust that people have in it. As users get more and more tech-savvy and gain experience online, they need to feel that their privacy and security are in good hands. 

This is where Content Moderation Trust and Safety programs become essential, and solid content moderation is a key element in them. In the following sections, you can find an overview of Trust and Safety principles for today’s dynamic digital landscape — and the role of moderation in ensuring their efficacy.

What Is a Trust and Safety Program?

Trust and Safety programs are not simply tools to meet legal standards and regulatory requirements. They are company plans which aim at positioning a digital platform as a trustworthy place which can offer a high level of protection to its users. 

In essence, a Trust and Safety program consists of precise guidelines on how to bring down the risks from using a platform to a minimum. The major threats include exposure to disturbing, inappropriate or offensive content, scams, fraud, bullying, harassment, insults, and similar. 

1. The Importance of Trust and Safety Programs

Putting in practice an effective Trust and Safety program is essential for the reputation and positioning of digital platforms today — from social media and online marketplaces to dating platforms and booking websites. People are increasingly more aware of the risks they can face online and prefer to opt in for websites and apps that have a solid track record. 

For digital platforms, complying with solid Trust and Safety requirements is the key to increasing user base, minimizing churn, and boosting the loyalty of current users. In business terms, Trust and Safety practices ultimately have a strong impact on the bottomline. Online businesses based on social communities and user-generated content rely heavily on the level of trust for the growth of their revenue, scaling, and global expansion. 

Protecting users on digital platforms is not only a smart business decision, though. It’s also a question of moral obligations towards vulnerable groups and the community as a whole. In more and more places around the world, safety and privacy are legal requirements that online businesses have to observe rigorously. 

2. Essentials for Trust and Safety Programs

When it comes to crafting working Trust and Safety programs that truly deliver on their promises, there are a number of considerations to keep in mind.

First and foremost, a functional program should be able to address the wide variety and the growing amount of potential violations. They’re not only abundant, but keep changing, as violators seek innovative ways to fulfill their goals and go around protection mechanisms. It’s also important to note that risks vary depending on the communication channels — which means different strategies may be necessary to address the growingly diverse safety and privacy threats. 

Additional considerations include the variety of languages that your digital platform boasts. With multilingual support, the challenges grow. In addition, even in a common language like English, inappropriate behavior and content can take many different shapes and forms. There are also cultural differences that can affect how trust and safety should be upheld.  

Content Moderation in the Context of Trust and Safety

One of the most powerful tools that Trust and Safety managers of digital platforms have in their hands is content moderation. 

The process of moderation entails the monitoring of content — text, images, video, and audio — with the aim of removing illegal and harmful items that pose risks to different groups and jeopardize the reputation of an online brand. Through these functions, content review is essential to ensuring the trust of users in and the required level of safety for guaranteeing protection from illegal and harmful items and actions. 

With effective content moderation, digital platforms can protect their users from:

• The sale of unlawful and dangerous goods
• Dissemination of hateful and discriminative ideas
• Radical and criminal behavior of other users 
• Exposure to gruesome visuals 

How to Boost Your Content Moderation Efforts

With the rapid growth of digital platforms and the immense amounts of content that need to be reviewed to ensure Trust and Safety, relying on manual content moderation can be daunting. In fact, it may prove practically impossible to ensure on-the-go moderation when the volume of content that goes online grows exponentially. 

Automatic content moderation powered by Artificial Intelligence is proving as the most appropriate and effective solution to this conundrum. It doesn’t mean fully replacing moderation teams with machines — but involves the use of powerful platforms to minimize and simplify the work for human moderators.

The automatic algorithms can remove items that are in direct contradiction with the rules and standards of a digital platform. This saves a ton of time and effort that otherwise a moderation team has to invest in sifting through the piles of content. When there are items that are contentious and the thresholds for removal are not reached, the moderation platform directs them for manual review. 

In addition to dramatically increasing the productivity and speed of the moderation process, automatic content moderation spares content moderators from having to look at the most disturbing and gruesome content. This contributes positively to the parameters of the job which is known for being high-risk and traumatizing for many. 

Imagga Helps You Deliver on Trust and Safety

Crafting and enacting a Trust and Safety program for your digital platform gets easier with effective content moderation. Imagga’s CM solution provides you with the right tools to protect your users from harmful and illegal content. 

With Imagga, you can handle all types of content, including text, images, video and even live streaming. The platform will monitor all posted content and will automatically remove items that are not compatible with your community guidelines. You can set thresholds for content flagging for items that need to be processed by human moderators. 

Our content moderation solution allows you to set content retention policies that further assist you in meeting Trust and Safety requirements, as well as official regulations. 

Want to get started? You can refer to our in-depth how-to guide or get in touch straight away to check out how Imagga can boost your Trust and Safety efforts.  

by Ralitsa Golemanova

How Platforms Can Keep Their Online Communities Safe? Our 4 Best Practices

The digital world has steadily become an indispensable part of our real lives — and with this, the numerous risks posed by online interactions have grown exponentially. 

Today online platforms where people can communicate or share user-generated content need to step up their protective measures. That’s necessary for ethical, reputational, and legal reasons.

For one, digital platforms have a responsibility to safeguard the interests of their users — and especially of vulnerable groups among them. Protecting online communities and providing an inclusive environment is imperative from the point of view of branding too. There’s also a growing body of national and international legislation requiring concrete safety measures from online platforms. 

Whether you’re just launching an online platform, or are looking for ways to boost the safety measures of an existing one, there are tried-and-tested practices you can implement. At their core, they’re targeted at preventing intentional and unintentional harm to your users and at protecting their privacy, safety, and dignity. 

How To Keep Your Online Communities Safe

We’ve compiled a list of proven tips for setting up a protection plan for your online community. They can help you get ahead of the threats — and minimize the necessary efforts. 

1. Check Your Platform Design for Risks

It’s way more effective and cheaper to take preemptive actions towards risks — rather than waiting for harm to happen and addressing it only then. 

That’s a solid guiding principle for your platform’s protection plan. You can apply it already during the design stage when you’re defining the features and functions you’d like to include.  

Most importantly, you can plan the functionalities of your platform with safety in mind. This includes considering:

• How ill-meaning users can abuse the platform and harm or defraud others — and how you can prevent that 
• How safety principles can be implemented in the creation of features and functions
• The types of harmful content that can be or is most likely to be generated on your particular platform
• How the interaction between the different functionalities can give rise to risks and harms for users
• How to set up an effective reporting and complaints system 
• Ways to automate the processes, taking care of users’ safety and minimizing the risk of exposure to unsafe content

2. Analyze Vulnerable Groups at Risk

The next essential step in your protection plan is to identify the groups using your platform that are at a higher risk from online harm. 

Most commonly, one of these groups is that of younger users. Naturally, they shouldn’t be exposed to unsafe and adult content. They should also be protected from the potential advances of older users. In some cases, you may even want to limit the age for the users of your platform. 

There are different ways to address the needs of younger users, such as:

• Setting up a trustworthy system for verifying the age of users 
• Creating access restrictions for younger users
• Establishing rules for interacting with underage users and enforcing restrictions through technology

Children are just one of many potential vulnerable groups. Your analysis may show you other types of users who are at an increased risk of harm while using your online platform. These may include differently-abled users, members of minorities, and others. 

Depending on the precise risks for each vulnerable group, it’s important to have tailored plans for protection for each of them. It’s also a good idea to improve the overall safety of your platform — so you can deliver an inclusive and protected environment for everyone.  

3. Set Up a Trust and Safety Program and a Dedicated Team

Building and implementing a sound Trust and Safety program has become an essential prerequisite for online platforms that want to protect their users and be in legal compliance. 

In essence, your Trust and Safety program is the command center of all your efforts to protect your users. It has to contain all your guidelines and activities intended to minimize risks connected with using your platform. 

Some crucial aspects to consider include:

• The wide variety of potential violations that can be committed
• The dynamic nature of potential violations which evolve with technological changes
• The different nuances in harmful and inappropriate user content and interactions
• The most effective approaches to content moderation as a central part of your program

Online platforms that take their Trust and Safety programs seriously have dedicated teams making sure that community rules are respected. This means having an overall approach to safety, rather than simply hiring a content moderation person or team.

Your safety team should:

• Know thoroughly the major risks that users face on your platform 
• Be knowledgeable about your protection practices 
• Have the right resources, training, and support to implement your Trust and Safety program effectively 

4. Don’t Spare Efforts on Content Moderation

Finally, we get to the essential protection mechanism for online platforms with user-generated content and communication: content moderation. It is an indispensable tool for keeping your community safe. 

Content moderation has been employed for years now by platforms of all sizes — from giants like Facebook and Twitter to small e-commerce websites. It entails the practice of filtering content for different types of inappropriate and harmful materials. Moderation can span text, visuals, video, and live streaming, depending on the specifics and needs of a platform. 

While content moderation is the key to a safe and inclusive online platform, it takes its toll on the people who actually perform it. It’s a burdensome and traumatic job that leaves many moderators drained and damaged. Yet, it’s indispensable.

That’s why automatic content moderation has become an important technological advancement in the field. Powered by Artificial Intelligence, moderation platforms like Imagga take care of the toughest part of the filtering process. Image recognition and text filtering allow the fast, safe, and precise elimination of the most disturbing content, whether it’s in written posts, photos, illustrations, videos, or live streaming. 

Naturally, there’s still a need for human input in the moderation process. But that’s possible too, by combining automatic filtering with different levels of engagement by human moderators. The blend between effective automation and expert insights gets better with time too, as the AI algorithms learn from previous decisions and improve with each moderation batch.  

Automating Your Content Moderation: How Imagga Can Help

Most safety measures are time-consuming and need specific members of staff to apply them. 

The good news is that the labor-intensive and traumatic content moderation can be easily automated — taking it to unseen speed and safety levels. This is what Imagga’s AI-powered automatic content moderation platform can deliver.

The moderation solution can be tweaked to match the exact needs of your platform at any given moment. For each of your projects that need content monitoring, you can set up priority levels, batch sizes, content and retention policies, and categories management. Most importantly, you can control the threshold levels for human moderation when borderline cases have to be reviewed by a moderation expert.

You can also easily modify the settings for your moderators by assigning them to different projects, and setting custom rules for them to follow, among others. The moderation process for your staff is simplified and as automated as possible. 

With Imagga, you can either provide the platform to your in-house moderation team, or you can hire an external team from us that will skillfully complement your protection measures. In both cases, the AI moderation algorithms do the heavy job, complemented by the minimum possible input from human moderators. 
Ready to give it a go? You can get in touch with us to find out how Imagga can help you protect your online community with its powerful content moderation solutions.

by Ralitsa Golemanova

Benefits of AI Content Moderation | Which Top Business Challenges Does It Solve?

User-generated content has transformed the internet — and online platforms where people can share text, visuals, videos and live streams are growing by the minute. 

This unseen scale of content creation in the last decade is impressive. It certainly brings new ways for self-expression for users, as well as new opportunities for businesses. At the same time, digital platforms can easily — and unwillingly — become hosts of certain users’ malicious intentions. From pornography and violence to weapons and drugs, harmful and illegal content gets published online all the time. 

This brings numerous challenges for online businesses relying on user-generated content, making moderation a requirement for ensuring a safe online environment. Platforms have to protect users and uphold their reputation — while making growth possible and complying with legal regulations.  In this article we'll go over the benefits of using AI content moderation compared to manual moderation.

Benefits Of AI Content Moderation

The complex task of managing content moderation at scale and in the rapidly evolving digital environment is made easier by moderation platforms powered by Artificial Intelligence. Solutions based on machine learning algorithms help businesses handle many of the challenges for which manual content review simply won’t do the job. 

Here are the most notable benefits of AI content moderation for your platform. 

1. Enabling Content Moderation at Scale

The biggest challenge for digital platforms based on user-generated content — from travel booking and dating websites to e-commerce and social media — is how to stay on top of all that content without automatic censoring and delays in publishing. 

The need for review of content is undoubtful, and platforms of all types and sizes are looking for viable solutions to execute it. However, manual moderation comes with a high price tag — both in terms of financial investment, as well as for human moderators’ well-being. It’s also quite challenging to enforce for certain types of content, such as live streaming, where moderation has to occur in real time and should include video, audio and text. 

Automatic content moderation complements the manual review approach and can increase its accuracy. It makes the process faster, saving tens and thousands of work hours. What’s more, AI-powered systems can go through massive amounts of information in record time, ensuring the speed and agility that modern digital platforms require. As algorithms learn from every new project — when human moderators take final decisions on dubious items and from the processing of huge volumes of content — AI can truly enable effective scaling for digital businesses. 

2. AI Handles What People Shouldn’t 

It’s not only expensive to hire and sustain a large moderation team when the amounts of posted content grow. It’s also a great risk for moderators who sift through the sheer volumes of posts and get exposed to the most disturbing and harmful content out there. 

The reports on the harm of content moderation on the psychological well-being of people are numerous. Stress, post-traumatic disorders, and desensitization are just some of the dire effects that moderators suffer from. The most popular social media like Facebook and Instagram have been criticized a lot about this in recent years. 

AI-powered moderation solutions don’t fully replace humans. Instead, they just take care of the hardest part of the job — the first round of screening. Moderation platforms can automatically remove content that is immediately recognized as illegal, graphic, noncompliant with a platform’s rules, or harmful in any other way. Only items that are questionable remain for manual moderation. This reduces the workload for content moderators and makes their job more manageable. This is probably one of the biggest benefits of using AI for content moderation.

3. Automatic CM Ensures Legal Compliance

Providing high-quality and timely content moderation is not only a matter of protecting users and your platform’s reputation as a secure online place. More and more legal requirements are enforced to safeguard people from exposure to harmful content. 

There are specific regulatory frameworks that digital platforms have to comply with, depending on where a business entity is based. The European Union’s Digital Services Act is setting the tone on the Old Continent, as are some individual states too. The US is also moving forward with requirements for effective content moderation that protects users. 

Automating big chunks of your moderation makes legal compliance easier. You can set automatic thresholds for removal of illegal content, which means it will be taken down in no time. The AI can also sift through content which would otherwise raise privacy issues if processed by people. Blurring of sensitive information can also be done to ensure private data protection. The whole content review process becomes faster, which minimizes the chances of non-compliance for your business and of unregulated exposure for users. 

4. Multilingual Moderation with AI Is Easier

Content moderation needs to be executed in different languages to meet the needs of global platforms and local websites alike. This applies to all types of content, including visual materials that contain words. 

The traditional moderation approach would be to hire or outsource to a team of moderators who have the necessary linguistic knowledge. This can be both difficult and expensive. It may also be quite slow — especially if multilingual moderators have to handle a couple of projects in different languages at the same time. 

With machine translation, moderation in languages other than English can be automated to a certain extent. Even when the process is handled by people, they can get immense support from the automatic translations and flagging.   

5. Automatic Moderation Allows On-the-Go Adjustments

Manual moderation can be somewhat slow to adjust to novel situations. When there is a new screening policy to apply, or new types of inappropriate content to include, this requires creating new procedures for content moderators. Often, introducing changes to the moderation process also requires re-training to help moderating staff stay on top of the most recent trends. 

AI-powered moderation can help with the flexibility challenge by allowing easy adjustments to content screening thresholds, project moderation rules, and many more variables. Automatic platforms can be tweaked in various ways to accommodate the current needs of the moderation process. 

In addition, when the automatic review is paired with human moderation, the process can become truly flexible. Batch sizes and priority levels for projects can be changed across the systems, making the accommodation process for moderators easier and faster. Assigning projects to the right team members is also enabled, leading to higher productivity and improved satisfaction among moderators with specific skill sets. 

How Imagga Can Help Your Online Platform?

Artificial Intelligence has fueled the creation of powerful content moderation platforms. Imagga’s CM solution is here to make the moderation challenge easier to handle. 

Imagga processes all types of content — from text and visuals to video and live streaming. You can manually set the types of inappropriate content you want to filter, as well as the thresholds for removal and referral to human moderators. In fact, you can control the whole process — and automate the parts that you want, while keeping the control you need. 

With Imagga, you can adjust content retention policies to comply with legal requirements. The platform also makes the moderators’ jobs easier by allowing different project priority levels and batches, as well as custom moderation rules and easy flagging process. 
Ready to give it a go? Take a look at our extensive how-to guide and get in touch to see how Imagga’s solution can solve your challenges related to content moderation.

by Ralitsa Golemanova

Image Background Removal API | What Is It? How Does It Work?

Image recognition based on Artificial Intelligence is changing the way many industries are going about their business — and it’s also making life easier for end consumers too. 

A great example of how machine learning algorithms come in handy in our day-to-day work and leisure activities is image background removal. Gone are the days when we manually had to erase the background and cut out objects from a photo with painstaking efforts. Automatic image background removal can get that done in no time — and with no manual work on our side. 

In the current guide, you can find out the essentials about picture background removal and how an API based on Artificial Intelligence can be of immense help in your visual tasks. 

What Is Image Background Removal? 

Professionals across many industries — including advertising, e-commerce, and marketing, among others — need to remove the background of images on a regular basis. The uses of background removal for photos are so numerous that it’s difficult to even list them — from social media and website images for brands to birthday greetings cards, and everything in between. 

In fact, image background removal is a necessity for a wide range of professionals today. Online stores, fashion and design websites, interior design websites, and content management systems are just a few of the extra examples — on top of traditional graphic designers and different types of digital marketers. Media content managers, developers and car dealers are a couple of other professionals whose jobs may entail image background removal. 

Background removal is also a necessity for many people who want to experiment with their personal photo collections. Users like to create image collages and make their extensive photo libraries come to life — be it for a family reunion presentation, a personalized greeting card, or a special gift to loved ones. 

Previously, the only way to extract the background from a visual was to use an advanced software solution like Adobe Photoshop. You had to cut out the desired objects by hand through selecting the contours around them via point setting. For inexperienced graphic designers, it was a slow and tedious process that often did not yield the desired results. It sometimes even ended up in strange cutouts that seemed as though done by a child. 

Visual solutions have advanced tremendously and Adobe Photoshop and Illustrator, as well as online tools like Canva, offer easier ways to tackle background removal. And yet, there’s still manual work to be done with precision and at least some experience.  

For graphic designers, photographers, and marketers who work on tight schedules, taking the time to remove image backgrounds by hand can turn into a nightmare. They need quick and effective methods for taking care of mundane tasks like this one — and ideally, with a single click.  

What Is an Image Background Removal API?

Instead of removing backgrounds from images by hand, you can use advanced AI-based solutions that get the job done with no fuss. That’s what, in essence, is a background remover API. 

1. How Does It Work?

The API is powered by machine learning algorithms that enable the so-called computer vision — which in turn allows for the quick and automatic recognition of objects in visuals. The software is trained to identify objects, people, places, elements, actions, backgrounds, and more. The more images are processed through the algorithm, the better it becomes at spotting different details. 

On the basis of these capabilities, image recognition can be applied for a wide variety of automation tasks — image categorization, cropping, color extraction, visual search, facial recognition, and many more, including seamless background removal. 

The AI-powered platform can recognize the objects and their outlines within an image with unseen accuracy — and to smoothly separate them from the background against which they have been photographed or placed. 

2. Benefits and Uses of an Image Background Removal API

The benefits of automatic background removal are numerous:

• High quality and utmost precision of object cutouts
• Fully automatic process with a single API request
• Processing of thousands of images at once via the API implementation
• Saving tons of time and money for businesses 
• Enabling creativity for professionals and regular users alike

As noted earlier, background removal is a common task that is required across different industries and activities. Automatic removal with an image recognition API is thus useful for many types of professionals, as well as for end users. 

Professional photographers and graphic designers may as well become the biggest fans of automatic background removal. They can focus their time on actually taking photos and creating visual assets rather than on their tedious technical processing — as the API can help optimize their workflows. 

Marketing and advertising experts are the next in row. Visuals are powering up all social media, and nowadays there are barely any posts without images because they perform much better than text. That’s why marketers rely heavily on them — and background removal is a common task they need to take care of before sharing a visual on their brand’s channel. 

Online shops, auto dealers, fashion and design platforms, and all kinds of e-commerce businesses are another set of common users for a background remover API. They all need to process visuals in order to sell their goods and services — and removing unwanted backgrounds from images is a typical task. With automatic removal, they can save time on these mundane jobs, while optimizing their online catalog for higher conversion. 

Last but not least, both traditional and online media need image background removal in their daily work. Editorial visuals often have to be cropped, cleaned, and processed in different ways, including removing any elements in the background. In particular, media content editors regularly have to separate objects and people from photos to include them in news reports — be it during an election campaign or for announcing a novelty.  

How to Use an API Background Removal?

Getting started with Imagga’s picture background removal API is a simple process. Sign up for our private beta testing.

Frequently Asked Questions

by Ralitsa Golemanova

Image Tagging | What Is It? How Does It Work?

The digital world is a visual one — and making sense of it is based on the premise of quick visual searching. Companies and users alike need effective ways to discover visuals by using verbal cues like keywords. Image tagging is the way to realize that, as it enables the classification of visuals through the use of tags and labels. This allows for the quick searching and identifying of images, as well as the adequate categorization of visuals in databases. 

For both businesses and individuals, it’s essential to know what their visual content contains. This is how people and companies can sort through the massive amounts of images that are being created and published online constantly — and use them accordingly. 

Here is what image tagging constitutes — and how it can be of help for your visual database. 

What Is Image Tagging?

From stock photography and advertising to travel and booking platforms, a wide variety of businesses have to operate with huge volumes of visual content on a daily basis. Some of them also operate with user-generated visual content that may also need to be tagged and categorized. 

This process becomes manageable through the use of picture tagging. It allows the effective and intuitive search and discovery of relevant visuals from large libraries on the basis of preassigned tags. 

At its core, image tagging simply entails setting keywords for the elements that are contained in a visual. For example, a wedding photo will likely have the tags ‘wedding’, ‘couple’, ‘marriage’, and the like. But depending on the system, it may also have tags like colors, objects, and other specific items and characteristics in the image — including abstract terms like ‘love’, ‘relationship’, and more. 

Once visuals have assigned keywords, users and businesses can enter words relevant to what they’re looking for into a search field to locate the images they need. For example, they can enter the keyword ‘couple’ or ‘love’ and the results can include photos like the wedding one from the example above. 

It’s important to differentiate between image tagging and metadata. The latter typically contains technical data about an image, such as height, width, resolution, and other similar parameters. Metadata is automatically embedded in visual files. On the other hand, tagging entails describing with keywords what is visible in an image.  

How Does Image Tagging Work?

The process of picture tagging entails the identification of people, objects, places, emotions, abstract concepts, and other attributes that may pertain to a visual. They are then ascribed to the visual with the help of predefined tags. 

When searching within an image library, users can thus write the keywords they are looking for, and get results based on them. This is how people can get easy access to visuals containing the right elements that they need. 

With the development of new technology, photo tagging has evolved to a complex process with sophisticated results. It not only identifies the actual items, colors and shapes contained in an image, but an array of other characteristics. For example, image tagging can include the general atmosphere portrayed in an image, concepts, feelings, relationships, and much more.  

This high level of complexity that image tagging can offer today allows for more robust image discovery options. With descriptive tags attached to visuals, the search capabilities increase and become more precise. This means people can truly find the images they’re after. 

Applications of Image Tagging

Photo tagging is essential for a wide variety of digital businesses today. E-commerce, stock photo databases, booking and travel platforms, traditional and social media, and all kinds of other companies need adequate and effective image sorting systems to stay on top of their visual assets. 

Image tagging is helpful for individuals too. Arranging and searching through personal photo libraries is tedious, if not impossible, without user-friendly image categorization and keyword discoverability. 

Types of Image Tagging

Back in the day, image tagging could only be done manually. When working with smaller amounts of visuals, this was still possible, even though it was a tedious process. 

In manual tagging, each image has to be reviewed. Then the person has to set the relevant keywords by hand — often from a predefined list of concepts. Usually it’s also possible to add new keywords if necessary. 

Today, image tagging is automated with the help of software. Automated photo tagging, naturally, is unimaginably faster and more efficient than the manual process. It also offers great capabilities in terms of sorting, categorizing and content searching. 

Instead of a person sorting through the content, an auto image tagging solution processes the visuals. It automatically assigns the relevant keywords and tags on the basis of the findings supplied by the computer vision capabilities. 

Auto Tagging

AI-powered image tagging — also known as auto tagging — is at the forefront of innovating the way we work with visuals. It allows you to add contextual information to your images, videos and live streams, making the discovery process easier and more robust. 

How It Works

Imagga’s auto tagging platform allows you to automatically assign tags and keywords to items in your visual library. The solution is based on computer vision, using a deep learning model to analyze the pixel content of every photo or video. In this way, the platform identifies the features of people, objects, places and other items of interest. It then assigns the relevant tag or keyword to describe the content of the visual. 

The deep learning model in Imagga’s solution operates on the basis of more than 7,000 common objects. It thus has the ability to recognize the majority of items necessary to identify what’s contained in an image. 

In fact, the image recognition model becomes more and more precise with regular use. It ‘learns’ from processing hundreds and thousands of visuals — and from receiving human input on the accuracy of the keywords it suggests. This makes using auto tagging a winning move that not only pays off, but also improves with time.  

Benefits

Automated image tagging is of great help to businesses that rely on image searchability. It saves immense amounts of time and effort that would otherwise be wasted in manual tagging — which may not be even plausible, given the gigantic volumes of visual content that has to be sorted.

Auto tagging allows companies not only to boost their image databases, but to be able to scale their operations as they need. With automated image tagging, businesses can process millions of images — which enables them to grow without technical impediments.  

Examples

You can try out our generic model demo to explore the capabilities of Imagga’s auto tagging solution. You can insert your own photo or use one of the examples to check out how computer vision can easily identify the main items in an image. 

In the example above, the image contains a pink flower with an insect on it. The image tagging solution processes the visual in no time, supplying you with tags containing major concepts. You also get the accuracy percentage of each identified term. 

The highest ranking ones typically include the basics about the image — such as the object or person and the main details about them. Further down the list of generated tags, you can also find colors, shapes, and other terms describing what the computer vision ‘sees’ in the picture. They can also include notions about space, time, emotions, and similar. 

Image Tagging for Adobe Lightroom

To make the life of professionals working with photography easier, Imagga created an automatic image tagging plugin for Adobe Lightroom — Wordroom. With its help, sorting through gigantic visual collections becomes faster, easier, and much more manageable. Photographers and editors can easily add keywords to each image, making it discoverable within the software. 

The plugin provides automated photo keywording based on computer vision technology. Wordroom can suggest up to 30 keywords thanks to its image recognition algorithm. Users can select the keywords they need or keep them all. 

As in other image tagging processes, the Wordroom plugin can recognize different elements within an image. These include people, objects, colors, shapes, and actions, as well as emotions, atmosphere, abstract concepts, and themes. 

Wordroom is optimized for use with stock photos, so it is of particular interest for stock photographers working with Adobe Lightroom. At the same time, it’s also available to regular users and other types of professionals too. The same AI-based technology powers Imagga’s image recognition and categorization solutions for stock photography and videography websites like Unsplash too. 

How to Improve Auto Tagging with Custom Training

The best perk of auto-tagging is that it can get better with time. The deep learning model can be trained with additional data to recognize custom items and provide accurate tagging in specific industries. 

With Imagga’s custom training, your auto-tagging system can learn to identify custom items that are specific to your business niche. You can set the categories to which visual content should be assigned. 

Custom training of your auto-tagging platform allows you to fully adapt the process to the particular needs of your operations — and to use the power of deep learning models to the fullest. In particular, it’s highly useful for businesses in niche industries or with other tagging particularities. 

Imagga’s custom auto-tagging can be deployed in the cloud, on-premise, or on the Edge. 

FAQ

by Ralitsa Golemanova

What Is a Content Moderator? Responsibilities, Skills, Requirements and more

The role of the content moderator in today’s digital world is a central one. Moderators take on the challenging task of reviewing user-generated content to ensure the safety and privacy of online platforms. They act, in a sense, as first-line responders who take care that our digital experiences are safe.  Read on to find out what is a content moderator!

The content moderation process, as a whole, is a complex one because it entails the thorough screening of various types of content that goes online. The purpose is to ensure the protection of platform users, safeguard the reputation of digital brands, and guarantee compliance with applicable regulations.  

In many cases, this means that content moderators have to go through every single piece of text, visual, video, and audio that’s being posted — or to review every report for suspicious content. 

What Is a Content Moderator?

Content moderators are crucial in the process of ensuring the safety and functionality of online platforms that rely on user-generated content. They have to review massive amounts of textual, visual, and audio data to judge whether it complies with the predetermined rules and guidelines for the safety of a website. 

Moderators help platforms uphold their Trust and Safety programs — and ultimately, provide real-time protection for their user base. Their efforts are focused on removing inappropriate and harmful content before it reaches users. 

In this sense, the role of content moderators is essential because their work shields the rest of us from being exposed to a long list of disturbing and illegal content, containing:

• Terrorism and extremism
• Violence 
• Crimes 
• Sexual exploitation 
• Drug abuse 
• Spam
• Scam
• Trolling
• Various types of other harmful and offensive content 

What Does a Content Moderator Do?

The job of the content moderator is a multifaceted one. While a large portion of it may consist of removing posts, it’s actually a more complex combination of tasks. 

On the practical level, content moderators use targeted tools to screen text, images, video, and audio that are inappropriate, offensive, illegal or harmful. Then they decide whether pieces of content or user profiles have to be taken down because they violate a platform’s rules or are outright spam, scam, or trolling.

In addition, content moderators may also reply to user questions and comments on social media posts, on brands’ blogs, and in forums. They can also provide protection from inappropriate content and harassment on social media pages. 

By doing all of this, moderators help uphold the ethical standards and maintain the legal compliance of digital businesses and online communities. Their timely and adequate actions are also essential in protecting the reputation of online platforms.    

As a whole, the job of the content moderator is to enable the development of strong and vibrant communities for digital brands where vulnerable users are protected and platforms keep their initial purpose.

What Types of User-Generated Content Does a Content Moderator Review?

The variety of user-generated content is growing by the day. This means that content moderators have to stay on top of all technological developments to be able to review them adequately. 

The main types of content that are being posted online today include text, images, video, and audio. They are the building blocks of all user-generated content. 

Yet the combinations between these formats are growing, with new ones emerging constantly. Just think of the news stories and live streams on platforms such as Facebook, Instagram, Twitter, and even LinkedIn. 

Content moderators may also review some other content formats, such as:

• User posts on forums 
• Product and service reviews on ecommerce platforms and on forums
• External links in social media posts
• Comments on blog posts

With the development of new technology, the types of user-generated content that may need content moderation screening is bound to grow — increasing the importance of the review process for digital platforms.

Alternative Solutions to Using a Content Moderator

In recent years, the gigantic volume of content that has to be reviewed has pushed for major technological advancements in the field. They have become necessary to address the need for faster moderation of huge amounts of user-generated posts that go live — and for unseen levels of scalability.

This has led to the creation and growing popularity of automated content moderation solutions. With their help, moderation becomes quicker and more effective. AI-powered tools automate the most tedious steps of the process, while also protecting human moderators from the most horrific content. The benefits of moderation platforms are undoubtful — and complement the qualified and essential work of people in this field. 

Imagga’s content moderation platform, in particular, offers an all-around solution for handling the moderation needs of any digital platform — be it e-commerce, dating, or other. The pre-trained algorithms, which also learn on the go from every new moderation decision, save tons of work hours for human moderators. Machine learning has presented powerful capabilities to handle moderation in a faster and easier way — and with the option of self-improvement. 

As noted, content moderation often cannot be a fully automatic process — at least at this stage of technological development. There are many cases that require an actual individual to make a decision because there are so many ‘grey areas’ when it comes to content screening.

Imagga’s platform can be used by an existing moderation team to speed up their processes and make them safer and smoother. The hard work is handled by the AI algorithms, while people have to participate only in fine-tuning contentious decisions. 

In practice, this means the platform sifts through all posted content automatically. When it identifies clearly inappropriate content that falls within the predefined thresholds, it removes it straight away. If there is content, however, that is questionable, the tool forwards the item to a human moderator for a final decision. On the basis of the choices that people make in these tricky cases, the algorithm evolves and can cover even larger expanses. 

Content Moderation Skills

While content moderation solutions have taken up a large part of the hardest work, the job of the content moderator remains irreplaceable in certain situations. It’s a role that is quite demanding and requires a wide range of skills. 

The basic task of the moderator is to figure out what content is permissible and what’s not — in accordance with the preset standards of a platform. This requires having sound judgment, so analytical skills are essential. 

To achieve this, moderators have to have a sharp eye for detail and a quick mind — so they can easily catch the elements within a piece of content that are inappropriate. On many occasions, it’s important to also be thick-skinned when it comes to disturbing content. 

The down-to-earth approach should be complemented with the ability to make the right contextual analysis. Beyond the universally offensive and horrible content, some texts and visuals may be inappropriate in one part of the world, while perfectly acceptable in another. 

In general, moderators should be good at overall community management, respecting the specificities and dynamics of particular groups. The best-case scenario is to have previous experience in such a role. This would equip one with the knowledge of communication styles and management approaches that preserve the core values of an online group. 

Multilingual support is often necessary too, having in mind the wide popularity of international platforms that host users from all over the world. That’s why moderators who know a couple of languages are in high demand. 

Last but not least, the content moderator's job requires flexibility and adaptability. The moderation process is a dynamic one — with constantly evolving formats, goals, and parameters. While complementing human moderators’ work, new technological solutions also require proper training. 

How to Become a Content Moderator?

As the previous section reveals, being a content moderator is not simply a mechanical task. In fact, it is a demanding role that requires a multifaceted set of skills. While challenging at times, it’s an important job that can be quite rewarding. 

To become a content moderator, one needs to develop:

• Strong analytical skills for discerning different degrees of content compliance
• Detail-oriented approach to reviewing sensitive content
• Contextual knowledge and ability to adapt decision-making to different situations and settings 
• A flexible approach to the moderation process, depending on emerging formats, trends and technology 

Moderators can work either for a specific brand or for a content moderation company that provides services for different types of businesses. This is the essential difference between in-house and external content moderation. The choice between the two options is a common conundrum — both for content moderators looking for a new position and for digital companies looking for a way to handle their moderation needs. 

In-house content moderators learn the nitty-gritty details for a single company. They become experts in dealing with the specific types of content that are published on a particular platform or on a brand’s social media channels. This typically makes them highly qualified in a certain industry.  

On the other hand, many companies choose to use external services from a content moderation provider instead of having in-house teams. Then moderators get assigned to different projects, which may be more than one at the same time. This can also be interesting because it entails varied work in a number of fields — and gaining different knowledge across the board. 

Frequently Asked Questions

Do you have any questions about what is a content moderator? Let us know in the comment section or don’t hesitate to reach out to us.

by Ralitsa Golemanova

Automated Content Moderation | What Is It? Benefits, tools and more

Due to the gigantic amounts of user-generated content that goes online continuously, it’s difficult to stay on top of content moderation. Plus, the risks of exposing human moderators to harmful content make manual moderation less and less desirable. This is where automated content moderation comes in. 

Content moderation is crucial for the functioning of digital platforms today. It provides a much-needed safety net that protects online users from harmful and illegal content. 

The reasons are numerous: online businesses have a moral obligation to protect their users from inappropriate content, uphold their brand reputation as a secure space, and comply with applicable regulations that require the removal of illegal and offensive materials. 

What Is Automated Content Moderation?

Automated content moderation entails the use of technology to speed up the removal of inappropriate and harmful content and to automate the tedious tasks of reviewing every single posting by hand. 

While named ‘automated’, it consists of a mixture between algorithms and a final human review in most cases. That’s why it’s sometimes referred to as a semi-automated. Technology does the heavy lifting, while the input of human moderators is necessary only after an automatic prescreening or in certain situations. 

Automating the process of content moderation is done by using AI-powered algorithms. They identify inappropriate content on the basis of previously fed data. 

The moderation platform filters content by recognizing illegal, sexually explicit or harmful elements in visuals, text, videos and even live streams. Depending on the thresholds for moderation, it may prompt for human input for cases that cannot be discerned by the AI. 

Benefits of Automated Content Moderation

The advantages of using automated moderation are numerous. The new technology has practically revolutionized how platforms based on user-generated content are handling these processes.

1. Speed

Our digital world requires a fast pace of moderation. No one would like to wait days before their social media post goes live because it has to be reviewed by a human moderator.

One of the biggest promises of automation — which it truly delivers on — is speed. Moderating the tons of content that go online every second seems like a mission impossible without technology. But with its help, it can occur in real time. 

With algorithms, the moderation process gets faster and more efficient. Content that is undoubtedly harmful or illegal can be immediately taken down. Dubious content automatically gets flagged and forwarded for human review. This makes the whole cycle quicker, providing for end users the immediacy of online media. 

2. Moderation at Scale

For online businesses, speed relates to scale too. If you’re running a platform that’s bound to grow, you need a viable moderation solution for handling an exponentially growing amount of user-generated content. 

Manual moderation wouldn’t be a feasible way to manage the huge amounts of content — it would require a very large team of human moderators and a very tight work schedule. The logical solution is automation, which can take over a huge chunk of the moderation process. 

Using automated content moderation helps digital platforms grow, while allowing them to preserve their security standards and to uphold their Trust and Safety programs. 

3. Protection of Human Moderators

A significant benefit of automated content moderation is related to the work of human moderators. It prevents them from having to go through the most disturbing content, as it gets automatically prescreened. 

The negative psychological effects of moderation are not a secret, and the job is deemed as a truly challenging one. The more these risks can be curbed, the better it gets for both moderators and the platforms they work for. 

Limitations of Automated Content Moderation

While it’s mostly a winning move, there are some drawbacks to using automation in the content moderation process. Still, with good planning, they can also be easily overcome. 

For the time being, the moderation process is not fully automated. Instead, a semi-automated approach is the best solution for now. The input of human moderators is still necessary for certain decisions and sensitive cases because of their ability for critical reflection. 

The hard work is being done by the algorithms, and people are involved only at the last stages. The content has been pre-screened, but needs a final yes or no by a moderator. While not fully automatic, this saves tremendous amounts of work hours, plus reduces the exposure to harmful content. 

While the moderation technology is still learning and being improved, there may be some mistakes in the identification of harmful content. Technology still struggles with recognizing context in certain cases. Some inappropriate content may slip through, while compliant content may get flagged. The platform needs to be updated regularly to include the latest news and cultural symbols. 

While all of this is challenging, AI- and machine learning-powered systems are becoming better and better at recognizing illegal and harmful content. They are learning with every new input they’re processing, which sets a high promise for their future performance. 

Another hurdle for automated content moderation is the limited ability of technology to grasp contextual variations in speech, images and in cultural norms as a whole. The use of certain words or slang phrases in one region may be perfectly fine, while they may be offensive in other places. Nuances and variations in language and behavior may also be difficult to catch for automated platforms. Contextual use of imagery can be a tough nut to crack too. 

How Does Automated Content Moderation Work?

Automated content moderation can be used in different ways, depending on the needs of your platform:

• Pre-moderation: algorithms screen all content before it goes live
• Post-moderation: content is screened shortly after it’s gone live; this is the most popular method
• Reactive moderation: users report posts for inappropriateness after they have been published  

Whichever method you choose, your first step will be to set your moderation policy. You’ll need to define the rules and types of content that have to be removed, depending on the overall strategy of your platform. Thresholds also have to be set, so that the moderation tool has clear demarcation when content violates your standards. 

In the most common case of post-moderation, all user-generated content is processed by the moderation platform. On the basis of the set rules and thresholds, clearly inappropriate content is immediately removed. Due to the automation, this can happen quite soon after it’s been published. Some items are considered trickier by the algorithm, and they are fed for human review. Content moderators access the questionable items through the moderation interface. Then they make the final decision to keep or remove the content.   

Whenever content is forwarded for manual moderation, the training data from the human moderators’ actions feeds back into the automated moderation platform. In this way, the AI learns from the subtleties in human decisions to remove or keep certain content. With time, the new learnings enrich the algorithms and make the automatic process more and more accurate. 

What Type of Content Can You Moderate Automatically?

You can use automated content moderation with all types of content — visual, textual, and even with moving images. 

1. Visuals

With the help of computer vision, automated platforms can identify inappropriate content in images through object detection mechanisms. They use algorithms to recognize unwanted elements and their position for an understanding of the whole scene. Offensive text can also be spotted, even if it is contained in an image. 

The types of inappropriate visuals you can catch with fully automated content moderation include:

• Nudity and pornography
• Self-harm and gore
• Alcohol, drugs, and forbidden substances
• Weapons and torture instruments
• Verbal abuse, harsh language, and racism
• Obscene gestures
• Graffiti and demolished sights
• Physical abuse and slavery
• Mass fights
• Propaganda and terrorism
• Infamous or vulgar symbols
• Infamous landmarks
• Infamous people
• Horror and monstrous images
• Culturally-defined inappropriateness

2. Text 

Natural language processing (NLP) algorithms can recognize the main meaning of a text and its emotional charge. Automated moderation can identify the tone of the text and then categorize it thanks to sentiment analysis. It can also search for certain keywords within textual content. Additionally, built-in knowledge databases can be used to predict the compliance of texts with moderation policies. 

Algorithms can screen for:

• Bullying and harassment
• Hate Speech
• Trolling
• Copyrighted text
• Spam and scam 
• Fraudulent text
• Pornographic text

3. Video

Video moderation requires the most complex process. The whole file has to be reviewed to ensure its compliance. Computer vision identifies inappropriate elements in the visual parts, while other algorithms are used to analyze the audio too. Automated content moderation is applicable even for live streaming where the screening process is in real time. 

Automated Content Moderation Solutions

Imagga’s content moderation platform provides you with all the tools you need to automate your moderation process. It’s a powerful and easy-to-use solution that you can integrate in your operations — and prepare your platform for scaling.

Imagga’s AI-powered pre-trained systems analyze all content on the basis of the moderation guidelines that you have set. Our API learns on the go too, so it improves with every project it processes. 

In the Admin Dashboard, you can create different categories of inappropriate content to look for and define the parameters for each. You can set priority levels for projects, as well as thresholds for flagging and forwarding content for human moderation. You can also control data retention length. 

The Moderation Interface is crafted to make your human moderators’ job easier. They get automatically prescreened content that they can review faster and with reduced risks because the most horrible content has already been removed. Moderators can use handy hotkeys and organize their work effectively in the interface.   

With Imagga’s content moderation platform, you can effectively ensure the protection of your users, your brand reputation, and your human moderators. You can use our tools in the cloud or on premise — and you can easily plug them in your current processes, whether you have an in-house or an outsourced moderation team. 

Automated Content Moderation Case Studies

1. Live Streaming Use Case

• Simultaneous moderation of live video streams is needed
• Can’t be done via manual moderation due to privacy concerns
• Automated moderation guarantees the privacy
• Done in short interval of time, and if a problematic stream is detected, escalated to the website admins to follow nsfw policies - sending warning or/and terminating the stream, etc

2. Dating website Use Case

• similar to above but images uploaded for profile, videos and live stream chat if supported 
• Different levels of moderation regarding country of operation and type of dating website. 
• Automated CM removes the privacy concerns as it might be very sensitive when it comes to dating websites

3. Travel web sites Use Case

• Moderation of both images and texts - travel sights are better thanks to the reviews visitors leave - moderation of the text and images/videos is needed 
• Automated CM makes possible real time publishing of reviews when they pass the auto CM filter

Frequently Asked Questions

by Ralitsa Golemanova

What Is Content Moderation? | Types of Content Moderation, Tools, and more

The digital world is in a constant state of flux, and one of its powerful propellers is user generated content. Today, people are more likely to trust the opinion shared by people online rather than information provided by businesses and institutions. Read on to learn to find out what is content moderation.

Unimaginable quantities of text, images and video are being published daily — and brands need a way to keep tabs on the content that their platforms host. This is crucial for maintaining a safe and trustworthy environment for your clients, as well as for monitoring social influences on brand perception and complying with official regulations. 

Content moderation is the most effective method for achieving all of that. It helps online businesses provide a safe and healthy environment for their users. 

What Is Content Moderation?

Content moderation refers to the screening of inappropriate content that users post on a platform. The process entails the application of pre-set rules for monitoring content. If it doesn’t satisfy the guidelines, the content gets flagged and removed. The reasons can be different, including violence, offensiveness, extremism, nudity, hate speech, copyright infringements, and similar. 

The goal of content moderation is to ensure the platform is safe to use and upholds the brand’s Trust and Safety program. Content moderation is widely used by social media, dating websites and apps, marketplaces, forums, and similar platforms.

Why Is Content Moderation Important?

Because of the sheer amount of content that’s being created every second, platforms based on user-generated content are struggling to stay on top of inappropriate and offensive text, images, and videos. 

Content moderation is the only way to keep your brand’s website in line with your standards — and to protect your clients and your reputation. With its help, you can ensure your platform serves the purpose that you’ve designed it for, rather than giving space for spam, violence and explicit content. 

Types of Content Moderation

Many factors come into play when deciding what’s the best way to handle content moderation for your platform — such as your business focus, the types of user-generated content, and the specificities of your user base. 

Here are the main types of content moderation processes that you can choose from for your brand. 

1. Automated Moderation

Moderation today relies heavily on technology to make the process quicker, easier and safer. AI-powered algorithms analyze text and visuals in a fraction of the time that people need to do that, and most of all — it doesn’t suffer psychological traumas from processing inappropriate content. 

When it comes to text, automated moderation can screen for keywords that are deemed as problematic. More advanced systems can spot conversational patterns and relationship analysis too. 

As for visuals, image recognition powered by AI tools like Imagga offers a highly viable option for monitoring images, videos and live streams. Such solutions identify inappropriate imagery and have various options for controlling threshold levels and types of sensitive visuals. 

While tech-powered moderation is becoming more and more precise and effective, it cannot fully obliterate human review, especially in more complex situations. That’s why automated moderation still uses a mixture between technology and human moderation. 

2. Pre-Moderation

This is the most elaborate way to approach content moderation. It entails that every piece of content is reviewed before it gets published on your platform. When a user posts some text or a visual, the item is sent to the review queue. It goes live only after a content moderator has explicitly approved it.

While this is the safest way to block harmful content, this process is rather slow and not applicable for the fast-paced online world. However, platforms that require a high level of security still employ this moderation method. A common example are platforms for children where security of the users comes first.  

3. Post-Moderation

Post-moderation is the most typical way to go about content screening. Users are allowed to post their content whenever they wish to, but all items are queued for moderation. If an item is flagged, it gets removed to protect the rest of the users. 

Platforms strive to shorten review times, so that inappropriate content doesn’t stay online for too long. While post-moderation is not as secure as pre-moderation, it is still the preferred method for many digital businesses today.

4. Reactive Moderation

Reactive moderation entails relying on users to mark content that they find inappropriate or that goes against your platform’s rules. It can be an effective solution in some cases. 

Reactive moderation can be used as a standalone method, or combined with post-moderation for optimal results. In the latter case, users can flag content even after it has passed your moderation processes, so you get a double safety net. 

If you opt to use reactive moderation only, there are some risks you’d want to consider. A self-regulating platform sounds great, but it may lead to inappropriate content remaining online for far too long. This may cause long-term reputational damage to your brand. 

5. Distributed Moderation

This type of moderation relies fully on the online community to review content and remove it as necessary. Users employ a rating system to mark whether a piece of content matches the platform’s guidelines.

This method is seldom used because it poses significant challenges for brands in terms of reputation and legal compliance. 

How Does Content Moderation Work?

To put content moderation to use for your platform, you’ll first need to set clear guidelines about what constitutes inappropriate content. This is how the people who will be doing the job — content moderators — will know what to mark for removal. 

Besides types of content that have to be reviewed, flagged and removed, you’ll also have to define the thresholds for moderation. This refers to the sensitivity level that content moderators should stick to when reviewing content. What thresholds you’ll set would depend on your users’ expectations and their demographics, as well as the type of business you’re running. 

Content moderation, as explained in the previous section, can take a few different forms. Pre-moderation, or reviewing content before it’s published, is usually considered too slow for today’s user generated content volume. That’s why most platforms choose to review content after it’s gone live, and it gets immediately placed on the moderation queue. 

Post-moderation is often paired with automated moderation to achieve the best and quickest results.  

What Types of Content Can You Moderate?

Moderation can be applied to all kinds of content, depending on your platform’s focus - text, images, video, and even live streaming. 

1. Text

Text posts are everywhere — and can accompany all types of visual content too. That’s why moderating text is one of the prerogatives for all types of platforms with user-generated content. 

Just think of the variety of texts that are published all time, such as:

• Articles
• Social media discussions
• Comments 
• Job board postings 
• Forum posts

In fact, moderating text can be quite a feat. Catching offensive keywords is often not enough because inappropriate text can be made up of a sequence of perfectly appropriate words. There are nuances and cultural specificities to take into account as well. 

2. Images

Moderating visual content is considered a bit more straightforward, yet having clear guidelines and thresholds is essential. Cultural sensitivities and differences may come into play as well, so it’s important to know in-depth the specificities of your user bases in different geographical locations. 

Reviewing large amounts of images can be quite a challenge, which is a hot topic for visual-based platforms like Pinterest, Instagram, and the like. Content moderators can get exposed to deeply disturbing visuals, which is a huge risk of the job. 

3. Video

Video has become one of the most ubiquitous types of content these days. Moderating it, however, is not an easy job. The whole video file has to be screened because it may contain only a single disturbing scene, but that would still be enough to remove the whole of it. 

Another major challenge in moderating video content is that it often contains different types of text too, such as subtitles and titles. They also have to be reviewed before the video is approved. 

4. Live Streaming

Last but not least, there’s live streaming too, which is a whole different beast. Not only that it means moderating video and text, but it has to occur simultaneously with the actual streaming of the content. 

The Job of the Content Moderator

In essence, the content moderator is in charge of reviewing batches of content — whether textual or visual — and marking items that don’t meet a platform’s pre-set guidelines. This means that a person has to manually go through each item, assessing its appropriateness while reviewing it fully. This is often rather slow — and dangerous — if the moderator is not assisted by an automatic pre-screening. 

It’s no secret today that manual content moderation takes its toll on people. It holds numerous risks for moderators’ psychological state and well-being. They may get exposed to the most disturbing, violent, explicit and downright horrible content out there. 

That’s why various content moderation solutions have been created in recent years — that take over the most difficult part of the job. 

Content Moderation Solutions

While human review is still necessary in many situations, technology offers effective and safe ways to speed up content moderation and to make it safer for moderators. Hybrid models of work offer unseen scalability and efficiency for the moderation process. 

Tools powered by Artificial Intelligence, such as Imagga’s content moderation solution, hold immense potential for businesses that rely on large volumes of user generated content. Our platform offers automatic filtering of unsafe content — whether it’s in images, videos, or live streaming. 

The platform allows you to define your moderation rules and to set thresholds on the go. You can tweak various aspects of the automated moderation to make the process as effective and precise as you need.

You can easily integrate Imagga in your workflow, empowering your human moderation team with the feature-packed automatic solution that improves their work. The AI-powered algorithms learn on the go, so the more you use the platform, the better it will get at spotting the most common types of problematic content you’re struggling with.

You can use Imagga’s platform in the way that best fits your work — either in the cloud, or as an on-premise solution. 

Frequently Asked Questions

Here are the hottest topics you’ll want to ask about content moderation. 

Do you have any questions about what is content moderation? Let us know in the comment section or don't hesitate to reach out to us.

by Ralitsa Golemanova