RelayFormer: A Unified Local-Global Attention Framework for Scalable Image and Video Manipulation Localization
arXiv:2508.09459v3 Announce Type: replace-cross Abstract: Visual manipulation localization (VML) aims to identify tampered regions in images and videos, a task that has become increasingly challenging with the rise of advanced editing tools. Existing methods face two central issues. The first is resolution diversity. Resizing or padding can distort subtle forensic cues and introduce unnecessary computational cost. The second is the difficulty of extending spatial models for images to spatio-temporal inputs in videos, which often results in maintaining separate architectures for the two data ty
The proliferation of advanced AI-powered editing tools necessitates more sophisticated methods for identifying manipulated visual content, making robust localization techniques crucial. This research addresses key limitations of existing methods in handling resolution diversity and extending image models to video.
Advanced visual manipulation localization is critical for maintaining trust in digital media, combating misinformation, and ensuring evidentiary integrity in an era of increasingly realistic deepfakes and AI-generated content. Its scalability and unified approach for images and videos enhance its practical application across various domains.
The RelayFormer framework unifies image and video manipulation localization with greater scalability and accuracy by resolving issues with resolution diversity and the spatial-temporal gap in existing methods. This makes it harder for malicious actors to create undetectable tampered visuals.
- · Digital forensics companies
- · Social media platforms
- · Intelligence agencies
- · Cybersecurity firms
- · Malicious deepfake creators
- · Propaganda operations
- · Creators of undetectable fake news
Improved detection capabilities for manipulated images and videos will enhance the integrity of online content and digital evidence.
Increased difficulty for malicious actors to spread misinformation through visual content could lead to a shift in their tactics towards more subtle or non-visual forms of manipulation.
The development of highly effective, scalable manipulation detection tools could eventually lead to new regulatory frameworks for content authenticity and digital provenance, impacting content creation and distribution across industries.
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Read at arXiv cs.AI