arXiv:2607. 18230v1 Announce Type: cross Abstract: Modern vision-language models (VLMs) have significantly improved image generation and editing capabilities, making pixel-level image tampering detection increasingly important yet challenging under cross-model and out-of-distribution shifts.
arXiv:2609.36882v1 Announce Type: new Abstract: Localizing AI-edited regions is essential for interpretable forensic analysis, but remains challenging due to subtle and spatially distributed artifact...
FUSED is a new framework that jointly detects and localizes AI-generated inpainting by combining low-level forensic cues with high-level semantic features through a sparsely-gated Mixture-of-Experts architecture. It predicts both an image-level manipulation score and a pixel-level mask of the inpainted region. On the OpenSDID cross-generator benchmark, FUSED outperforms existing methods, especially on unseen generators, and transfers effectively to the AutoSplice and CocoGlide benchmarks, doubling localization performance.
The paper introduces TIGA, a source‑image‑free, training‑free attack that injects adversarial properties into a diffusion model’s sampling trajectory to evade black‑box AIGC forensic detectors. TIGA aggregates gradients from white‑box surrogate detectors to create a transferable prior, then uses anisotropic directional search with finite‑difference queries to estimate and stabilize directions for the DDIM trajectory, applying frequency‑domain reshaping to reduce artifacts. Experiments demonstrate strong black‑box attack performance, transferability, and robustness to post‑processing while maintaining high perceptual quality.
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.
arXiv:2610.01778v1 Announce Type: new Abstract: Reliable evaluation of image forgery localization (IFL) requires assessing models under diverse distribution changes, yet existing benchmarks often cov...
The paper introduces a multi‑view, confusion‑guided ensemble framework for synthetic image attribution, combining FFT‑ConvNeXt, DINOv2, CLIP, and Xception to capture frequency, semantic, and forensic cues. Extensive data augmentation simulates realistic post‑processing, while a binary expert classifier and class‑adaptive confidence calibration address ambiguities between similar diffusion models. The approach achieved 99.53% on the public leaderboard and 99.20% on the private leaderboard for the ICANN 2026 DLMMDD Workshop challenge.
arXiv:2608.30656v1 Announce Type: new Abstract: Proactive tamper localization embeds an imperceptible signal into an image prior to distribution, enabling pixel-level manipulation detection. Existing...
The paper proposes a unified forensics framework that extends traditional binary image manipulation detection to a multiclass setting—distinguishing real, fully synthetic, and tampered images. It adds a segmentation branch for pixel‑level localization of tampered regions, achieving higher classification accuracy and IoU scores compared to recent benchmarks. The authors provide the implementation on GitHub for reproducibility.
EIB-Net is an Entropy‑Guided Information Bottleneck Network designed to detect AI‑generated images across diverse generative models. It introduces an Image Entropy metric to automatically select the most informative, low‑entropy patch and applies a Variational Information Bottleneck to learn compact, generalizable features. Experiments on DIFF, DiffusionForensics, and GenImage benchmarks show state‑of‑the‑art performance, achieving 85.7% accuracy with only 2% of training data and maintaining robust cross‑generator generalization.
The paper introduces DeformView, a wide‑baseline multi‑view dataset with pixel‑level annotations of geometric inconsistencies, and evaluates existing multi‑view consistency‑scoring methods, finding they transfer poorly to forensic localization tasks. To address this, the authors propose DEFECt3R, a lightweight learning‑based classifier that leverages cross‑view feature relationships and hard negative supervision to localize inconsistencies at the pixel level, achieving better performance and fewer false positives. Ablation studies confirm the importance of feature representations and correspondence quality for localization.
arXiv:2608.20929v1 Announce Type: new Abstract: AI-generated image manipulation localization identifies edited pixels, but its OOD performance lags behind image-level detection partly because pixel s...