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VAAS: Vision-Attention Anomaly Scoring for Image Manipulation Detection in Digital Forensics
Congratulations to Opeyemi Bamigbade and co-authors Mark Scanlon, and John Sheppard on the publication of VAAS: Vision-Attention Anomaly Scoring for Image Manipulation Detection in Digital Forensics in Forensic Science International: Digital Investigation.
Co-authors: Mark Scanlon, and John Sheppard.
AI-generated summary of the contribution: This paper introduces Vision-Attention Anomaly Scoring (VAAS), a novel dual-module framework for image manipulation detection in digital forensics. VAAS integrates global attention-based anomaly estimation using Vision Transformers (ViT) with patch-level self-consistency scoring derived from SegFormer embeddings. The hybrid formulation provides a continuous and interpretable anomaly score that reflects both the location and degree of manipulation. Evaluations on the DF2023 and CASIA v2.0 datasets demonstrate that VAAS achieves competitive F1 and IoU performance, while enhancing visual explainability through attention-guided anomaly maps. The framework bridges quantitative detection with human-understandable reasoning, supporting transparent and reliable image integrity assessment.