arXiv Computer Vision
GAP-SAM: A Global Artifact Prior for Generalizable AI-Generated Image Manipulation Localization
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arXiv Computer Vision
Aug 31
FUSED: Forensic-Semantic Mixture-of-Experts for AI Inpainting Detection and Localization
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.
arXiv AI
Jul 21
Simple Domain Generalization for Strong Pixel-Level Image Tampering Detection in Modern VLMs
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 Computer Vision
Sep 3
DailyBench: A Unified Benchmark for AI-Generated and Manipulated Images from Modern Generative Models
DailyBench is a unified benchmark designed to evaluate AI-generated image detectors on both modern full-image synthesis and object-level manipulation. It comprises two subsets: FakeBench, featuring high‑quality images from recent open‑source and commercial generative models, and ManipulationBench, containing subtle local edits applied to real images using advanced image‑conditional models. Experiments show that detectors with high accuracy on older datasets perform poorly on DailyBench, revealing significant robustness gaps.
arXiv Computer Vision
Sep 14
Semantically Aligned Gradient-Driven Context-Preserving Image Editing
Semantically Aligned Gradient-Driven Context-Preserving Image Editing (IABEdit) is a model‑agnostic framework that embeds differentiable semantic verification into the training of generative image editors. By using a frozen vision‑language model to extract spatially‑aware descriptors from ground‑truth edits and a trainable aligner to reproduce them from generated outputs, the residual becomes a gradient that teaches the generator both what to edit and where, without adding inference‑time VLM cost. IABEdit is compatible with various backbones (e.g., U‑Net in Stable Diffusion and MMDiT in FLUX) and improves structural fidelity on MagicBrush, achieves state‑of‑the‑art instruction adherence on RealEdit and EMU Edit, and outperforms the proprietary Gemini agent on the D‑LORD surveillance benchmark under heavy occlusion. "whyItMatters":"IABEdit demonstrates that incorporating semantic verification during training can produce more accurate, well‑localized edits and outperform existing methods even in challenging surveillance scenarios, as shown by its superior metrics and human/GPT‑4o evaluations."
arXiv Computer Vision
Sep 10
Enhanced Deformable Convolution with Center-invariant Offset and Edge-aware Mask
arXiv:2609.10387v1 Announce Type: new Abstract: Deformable convolution networks have recently become popular for many computer vision tasks, especially for semantic segmentation, because of their exc...
arXiv AI
Jul 14
On Occlusions in Video Action Detection: Benchmark Datasets And Training Recipes
arXiv:2410. 19553v2 Announce Type: replace-cross Abstract: This paper explores the impact of occlusions in video action detection.