Learning Universal Features for Generalizable Image Forgery Localization
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
The Flow has not summarised this story yet — read it at arXiv Computer Vision.
arXiv:2502. 19716v3 Announce Type: replace-cross Abstract: Recent advances in visual generative models have enabled the creation of highly realistic, fully AI-generated images without relying on real source content.
The paper introduces DDL, a large-scale dataset for deepfake detection and localization, comprising over 1.4 million forged samples generated by 80 distinct deepfake methods. It features comprehensive coverage of generation architectures, varied manipulation modes, diverse scenarios and modalities, and provides fine-grained spatial masks and temporal segments for each forgery. These enhancements aim to offer a more challenging benchmark and support the development of next-generation deepfake detection, localization, and interpretability methods.
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.
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.
UniShield is a multi‑agent framework that unifies forgery image detection and localization across diverse domains such as image manipulation, document manipulation, DeepFake, and AI‑generated images. It combines a perception agent that analyzes image features to select appropriate detection models with a detection agent that integrates multiple expert detectors into a single system, producing interpretable reports. Experiments demonstrate that UniShield outperforms existing unified approaches and domain‑specific detectors, achieving state‑of‑the‑art performance and greater practicality, adaptiveness, and scalability.
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.