RelayFormer: New AI Framework for Detecting Manipulated Images and Videos
Researchers have proposed RelayFormer, a unified deep learning framework designed to localize tampered regions in both images and videos without requiring uniform resizing or separate model architectures. The system introduces Global Local Relay (GLR) tokens that propagate contextual information through a relay-based attention mechanism, allowing it to handle variable resolutions and temporal video data within a single model. As AI-powered editing tools become more sophisticated, scalable and accurate manipulation detection methods are increasingly important for media integrity.
RelayFormer is a new visual manipulation localization (VML) framework presented in a preprint on arXiv that addresses two persistent problems in the field: the distortion of forensic cues caused by resizing or padding images to uniform dimensions, and the difficulty of adapting image-based spatial models to handle video's additional temporal dimension. The framework partitions inputs into fixed-size sub-images and uses novel Global Local Relay (GLR) tokens to efficiently exchange both global semantic context and fine-grained local artifact information through a relay-based attention mechanism. This design allows a single architecture to process both static images and video sequences without the computational overhead typically associated with high-resolution or multi-frame inputs. The authors report superior performance across multiple benchmarks compared to prior approaches, while maintaining favorable computational efficiency. The code has been made publicly available, and the paper has undergone at least three revisions since its initial submission in August 2025.
What's missing
The paper does not specify which benchmarks were used for evaluation or provide quantitative performance figures in the abstract, making independent assessment of the claimed superiority difficult.
What different sources said
- arXiv cs.AICenter
RelayFormer: A Unified Local-Global Attention Framework for Scalable Image and Video Manipulation Localization
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