All articles tagged with #watermarking
The 2026 ICML used hidden watermarks in submitted papers to detect AI-generated peer reviews; 497 papers (about 2%) were rejected for violations of the conference's LLM-use policy, and organizers introduced two review streams—one permitting limited LLM use and one banning it—to protect trust in the peer-review process.
Cornell researchers have developed a novel invisible watermarking technique called noise-coded illumination that embeds unique light patterns into scenes during recording, enabling verification of video authenticity and detection of deepfakes by analyzing inconsistencies in the lighting codes, which are imperceptible to the naked eye and effective across various environments and skin tones.
Meta has introduced Meta Video Seal, a tool for watermarking AI-generated videos to combat the rise of deepfakes, which have significantly increased in prevalence. The tool, available in open source, aims to provide robust watermarking that withstands video compression and common edits. Despite existing solutions like DeepMind's SynthID and Microsoft's methods, Meta claims its tool offers superior resilience and scalability. However, adoption challenges remain, as many developers use proprietary solutions. Meta is also launching a leaderboard and organizing a workshop to encourage industry collaboration.
Meta has launched Imagine with Meta, a standalone generative AI experience that allows users to create high-resolution images by describing them in natural language. Similar to OpenAI's DALL-E, Imagine with Meta is powered by Meta's Emu image generation model and generates four images per prompt. Meta plans to add invisible watermarks to the generated content for increased transparency and traceability. The company aims to bring invisible watermarking to more of its products with AI-generated images in the future. The move comes as pressure mounts on tech firms to clearly indicate when works are generated by AI.
Amazon has introduced its Titan text-to-image AI model, Titan Image Generator, which allows developers to create realistic images with built-in guardrails against toxicity and bias. The model, aimed at an enterprise audience, offers the ability to generate images from natural language prompts and change backgrounds. All images generated by Titan Image Generator will include invisible watermarks as part of Amazon's voluntary commitments. The watermark can be detected using an API, and developers have the flexibility to choose how to provide that information to users. Additionally, Amazon announced the availability of other Titan models and extended copyright indemnity to customers using its foundation models.
Researchers at the University of Maryland have found that current AI watermarks are easily removable, raising concerns about the effectiveness of watermarking technology in combating deepfakes and misinformation. The study revealed that existing methods of watermarking can be evaded and fake emblems can be added to non-AI generated images. However, the researchers also developed a near-impossible-to-remove watermark that could help detect stolen products. Another collaborative research effort between the University of California, Santa Barbara and Carnegie Mellon University found that watermarks can be easily eliminated through destructive and constructive attacks. As AI-generated content becomes more prevalent, the need for improved watermarking technology to combat misinformation becomes crucial.
Researchers at the University of Maryland have found that current AI watermarking methods are easily defeated, making them unreliable for identifying AI-generated images and text. The study demonstrates how attackers can remove watermarks and even add watermarks to human-generated images, triggering false positives. Watermarking has been considered a promising strategy to combat misinformation and deepfakes, but researchers have consistently pointed out its major shortcomings. While some believe that watermarking can still be part of the solution when combined with other technologies, others argue that it is not effective and can be easily faked or removed.
Researchers at the University of Maryland have found that current AI watermarking methods are easily defeated, making them unreliable for identifying AI-generated images and text. The study demonstrates how attackers can remove watermarks and even add watermarks to human-generated images, triggering false positives. Watermarking has been considered a promising strategy to combat misinformation and deepfakes, but researchers have consistently pointed out its major shortcomings. While some believe that watermarking can still be part of the solution when combined with other technologies, others argue that it is not effective and can be easily faked or removed.
DeepMind, in collaboration with Google Cloud, has introduced SynthID, a tool for watermarking and identifying AI-generated images. SynthID, currently available in beta for select users of Vertex AI, embeds a digital watermark into the pixels of an image, making it detectable by an algorithm but imperceptible to the human eye. The tool supports Imagen, Google's text-to-image model, and aims to empower users to identify AI-generated content and prevent the spread of misinformation. While SynthID is not foolproof against extreme image manipulations, it provides a promising approach for responsible use of AI-generated content.