What lies ahead in 2025 for image recognition and its wide application in content moderation? 

In this blog post, we delve into the most prominent trends that will shape the development of AI-driven image recognition and its use in content moderation in the coming years, both in terms of technological advancements and responding to perceived challenges. 

Image recognition is at the heart of effective content moderation, and is also the basis of key other technological applications such as self-driving vehicles and facial recognition for social media and for security and safety. It’s no wonder that the global image recognition market is growing exponentially. In 2024 it was valued at $46.7 billion and is projected to grow to $98.6 billion by 2029. 

As a myriad of practical uses of AI across the board explode, including AI video generation, deepfake, and synthetic data model training, content moderation will become more challenging — and will have to rely on the evolution of image recognition algorithms. At the same time, the plethora of issues around AI ethics and biases, paired with the more stringent content moderation regulations on national and international level, will drive a significant growth in image recognition’s power and scope. 

Without further ado, here are the nine trends we believe will shape the path of image recognition and content moderation in 2025. 

AI Video Generation Goes Wild 

AI-powered video generation started going public in 2022, with a bunch of AI video generators now available to end users. Platforms like Runway, Synthesia, Pika Labs, Descript and more are already being used by millions of people to create generative videos, turn scripts into videos, and edit and polish video material. It’s safe to say AI video generation is becoming mainstream. 

In 2025, we can expect this trend to set off massive changes in how videos are being created, consumed, and moderated. As generative AI tools make giant leaps, AI video generators will become even more abundant and more precise in creating synthetic videos that mimic life. Their use will permeate all digital channels that we frequent and will become more visible in commercials and art forms like films. 

At the same time, the implications of these changes for the film industry and for visual and cinema artists will continue to raise questions about ownership and copyright infringements, as well as about the value of human creativity vs. AI generation. 

Real-Time Moderation for Videos and Live Streaming

AI-powered image recognition is already at the heart of real-time moderation of complex content like videos and especially live streaming. It allows platforms to ensure a safe environment for their users, whether it’s social media, dating apps, or e-commerce websites. 

As we move forward into 2025, we can expect real-time moderation to become ever more sophisticated to match the growing complexity and diversity of live video streaming. AI moderation systems will grow their capabilities in terms of contextual analysis needed to grasp nuances and tone, as well as in terms of multimodal moderation of video, audio and text within livestreams. They will also get better at predicting problematic content in streams. 

Moderation is also likely to follow the pattern of personalization that’s abundant in social media and digital communications. Platforms may start allowing users to choose their own moderation settings, such as levels for profanity and the like.

Multi-Modal Foundational AI Models 

Multi-modality is becoming a necessity in understanding different data types at the same time, including visual and textual. They combine visual, audio, text and sensor data to grasp the full spectrum and context of content. Multi-modal foundational AI models are advanced systems that are able to put together different data inputs into one in order to bring out deeper levels of meaning and help decision-making processes in various settings. 

In 2025, multi-modal AI models will grow, integrating many different types of data for all-round understanding. Cross-modality understanding will thus gradually become the norm for AI interactions that will grow to resemble human ones. 

Multi-modal AI models will bring about new applications in robotics, Virtual Reality and Augmented Reality, accessibility tools, search engines, and even virtual assistants. They will enable more comprehensive interactions with AI that are closer to human experience.  

Deepfake and AI-generated Content Detection

The spread of deepfake and AI-generated content is no longer a prediction for the future. Deepfakes are all around us and while they might be entertaining to watch when it comes to celebrities and favorite films, their misuse in representing politicians, for example, is especially threatening. Discerning between authentic and AI-generated content is becoming more and more complex, since AI becomes better at it.  

As people find it harder to tell the truth from fiction, detection of AI-generated content and deepfakes will evolve to meet the demand — the AI vs. AI battle. AI-powered detection and verification tools will become more sophisticated and will provide real-time identification, helping people differentiate content on the web in order to prevent fraud, scams, and misuse. 

We can expect to see the development of universal standards for detection and marking of AI-generated content and media, as well as databases with deepfakes that will help identification of fraudulent content. As the issue circulates in the public’s attention, people will also get better at identifying fake materials — and will have the right tools to do so.

AI-Driven Contextual Understanding

Comprehensive understanding of content, including context, tone, and intent, is still challenging for AI, but that is bound to change. We expect AI to get even better at discerning the purpose and tone of content — grasping nuances, humor, and satire, as well as harmful intentions. 

Contextual understanding will evolve to improve the accuracy of AI systems, as well as our trust in them. We’ll see more and more multi-modal context integration that enables all-round understanding, including intent and nuances detection. Cultural specificities will also come into play, as AI models are already embedding cultural contexts and differences. 

In 2025, we can also expect AI to develop further in spotting emotions and impact in content, getting closer to our human perception of data. Developments in contextual understanding will be driven by AI learning on-the-go from our changing trends in language and social norms. 

Use of Generative AI and Synthetic Data for Models’ Training

AI models’ training relies on large amounts of data — which are often scarce or difficult to obtain for a variety of reasons, including copyright and ethical concerns. 

Generative AI and synthetic data are already fueling models’ training, and we can expect to see more of that in the coming years. For example, synthetic data for content moderation is already in use, bringing about benefits like augmentation and de-biasing of datasets. However, it still requires in-depth supervision and input from real data.

Synthetic data will advance, creating accurate and hyper-realistic datasets for training. They will continue to help reduce bias in real-world datasets, plus will offer a way to scale and develop models that are working in more specific contexts.

AI-Human Hybrid Content Moderation 

Content moderation is the norm in various digital settings, from social media and dating apps to e-commerce and gaming. Its development has been fueled by the application of AI-driven image recognition across the board — and this trend will continue, combining multi-modality and context understanding advancements. 

At the same time, the AI-human hybrid content moderation is likely to stay around, especially when it comes to moderation of sensitive and culturally complex content. The speed and scale that AI offers in content moderation, particularly in live streaming and video, will continue to be paired with human review for accuracy and nuance detection. 

Betting on the hybrid approach will make content moderation achieve even greater efficiency and balancing, accounting for ethical and moral considerations as they appear. As AI trains on human decisions, its ability to discern complex dilemmas will also evolve. 

More Stringent Content Moderation Regulations

Legal requirements for content moderation are tightening, making digital platforms more accountable for the content they circulate and allow users to publish. As technology evolves, various threats evolve too — and national and international regulatory bodies are trying to catch up by adopting new legislation and enforcement methods. 

In particular, the most important recent content moderation regulations bringing about higher transparency and accountability requirements include the EU’s Digital Services Act (DSA), fully enforced in February 2024, and the General Data Protection Regulation (GDPR) which is in action from 2018. In the USA, this is Section 230 of the Communications Decency Act, while in the UK — Online Safety Bill that was enforced at the end of 2023. The EU’s AI Act will come into effect in 2025, and in full force — in 2026. 

We can expect the stricter legal requirements trend to continue in the coming years, as countries struggle to create an intentional framework for handling harmful content. AI accountability, fairness, transparency, and moral and ethical oversight will be certainly on the list of legislators’ demands. 

Ethics and Bias Mitigation

As stricter regulations come into force in 2025, like the EU’s AI Act, people will demand more comprehensive ethical and bias mitigation. This is becoming especially important in the context of security surveillance based on facial recognition, as well as in biases in AI generative models. 

Along with legal requirements, we’ll see new ethical frameworks for AI and image recognition in particular being developed and applied, which might even evolve into ethical certifications. Besides fair use, inclusivity and diversity will also become focal points that will be included in ethical guidelines. All of this will require the efforts of experts from the AI, law, and ethics fields. 

Bias mitigation will rely on diversifying datasets for model training, be it real-life or synthetic. The goal will be to decrease biases based on gender, race, or cultural differences, which can also be helped by automated bias auditing systems and by real-time bias detection. 

Discover the Power of Image Recognition for Your Business

Imagga has been a trailblazer in the image recognition field for more than a decade. We have become the preferred tech partner of companies across industries, empowering businesses to make the most of cutting-edge AI technology in image recognition. 

Get in touch to find out how our image recognition tools — including image tagging, facial recognition, visual search, and adult content detection — can help your business evolve, stay on top of image recognition trends, and meet industry standards.