arXiv Computer Vision
Support-guided OOD Removal with Diffusion
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arXiv AI
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AI-Generated Image Detectors Exploit Global Artifacts: Evidence from Inpainting Exchange
The paper demonstrates that diffusion‑based inpainting introduces a subtle, global spectral shift across the entire image, which current AI‑generated image detectors fail to detect. By creating a model‑free diagnostic called Inpainting Exchange (INP‑X) that restores unedited pixels while keeping synthesized content, the authors show that many pretrained detectors, commercial APIs, and bias‑aware methods collapse to chance performance. A large benchmark of 30K triplets across four datasets and three inpainters confirms the prevalence of this artifact, and a perceptual study indicates humans also struggle to spot the shift.
arXiv Computer Vision
Sep 2
Towards Generalized Image Manipulation Localization via Score-based Model
arXiv:2605.16879v2 Announce Type: replace Abstract: With the rapid evolution of synthetic media, Image Manipulation Localization (IML) has emerged as a critical component in multimedia forensics for...
arXiv Computer Vision
Oct 2
VITA: A Multi-Source Vicinal Transfer Augmentation Method for Out-of-Distribution Generalization
The paper introduces VITA, a multi‑source vicinal transfer augmentation method designed to improve out‑of‑distribution generalization in computer vision. VITA combines tangent transfer, which generates initial augmented samples to enhance robustness against various image corruptions, with an integration step that uses a generative model to produce on‑manifold samples from multiple vicinal sources. Experiments on corruption benchmarks show that VITA outperforms existing state‑of‑the‑art augmentation techniques.
arXiv AI
Sep 16
What Breaks Local Watermarks? A Robustness Benchmark for Local Invisible Image Watermarking
The paper introduces the first systematic robustness benchmark for local invisible image watermarking, evaluating five methods across 55 image transformations that include signal distortions, coordinate misalignments, indirect local edits, and direct watermark edits. Results show that all methods are vulnerable to some transformation, with MaskWM achieving the best payload recovery and localization but at the cost of image quality. The study highlights that robustness varies strongly with transformation type, especially noting that geometric misalignment and generative local edits can completely disrupt payload recovery.
arXiv Computer Vision
Sep 15
Frequency-Guided Diffusion Model with Perturbation Training for Skeleton-Based Video Anomaly Detection
arXiv:2412.03044v3 Announce Type: replace Abstract: Video anomaly detection (VAD) is a vital yet complex open-set task in computer vision, commonly tackled through reconstruction-based methods. Howev...
arXiv AI
Jun 2
Order within Chaos: Capturing Intrinsic Energy Anomalies for AI-Manipulated Image Forgery Localization
arXiv:2606. 02178v1 Announce Type: cross Abstract: Recent advancements in generative AI have led to image editing models capable of producing realistic forgeries that evade traditional image forgery localization methods, as these approaches depend on physical noise absent in synthetic data.