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.
By Anton Nuzhdin, Marcel Worring, Ivona Najdenkoska
arXiv:2512.17730v2 Announce Type: replace
Abstract: Detectors of AI-generated images tend to inherit the biases of the data they are trained on: models fitted to GAN imagery learn to treat GAN-specif...
By Yichen Jiang, Mohammed Talha Alam, Sohail Ahmed Khan, Duc-Tien Dang-Nguyen, Fakhri Karray
arXiv:2607. 16283v1 Announce Type: cross Abstract: The rapid advancement of generative AI has outpaced our ability to reliably detect its outputs, particularly when detectors encounter generators they have not seen before.
By Md Faraz Kabir Khan, Saeed Anwar, Ghulam Mubashar Hassan
arXiv:2607. 13234v1 Announce Type: cross Abstract: Deepfake detectors that achieve near-perfect scores on academic benchmarks collapse on real-world content: recent in-the-wild evaluations report AUC drops of 45-50% for state-of-the-art open-source models.
By Ken Jon Miyachi, Dylan Uys
The paper introduces a structured‑prior‑guided diffusion inpainting framework that enhances traffic sign augmentation by incorporating semantic, appearance, and geometric priors through text prompts, vector templates, and ControlNet. It enforces physical consistency with colour and edge‑structure losses, achieving superior reconstruction fidelity and OCR accuracy compared to larger models. The synthetic data generated improves detection performance for rare traffic sign classes by up to 7.4× over a real‑data‑only baseline.
By Luo Li, Chongchong Huang, Jun Jia, Qiang Gao, Xinlong Liu, Gui Yang, Liang Cao
The study evaluates 16 AI‑generated image detection methods in a zero‑shot setting, testing 23 pretrained variants on 12 datasets with 2.6 million images from 291 generators. Results show no single detector consistently outperforms others, with accuracy ranging from 37.5 % to 75.0 %. The study highlights that training data alignment and generator type significantly affect performance, especially against modern commercial models, and identifies systematic failure patterns across datasets.
By Simiao Ren (Neo), Yuchen Zhou (Neo), Xingyu Shen (Neo), Kidus Zewde (Neo), Tommy Duong (Neo), George Huang (Neo), Hatsanai (Neo), Tiangratanakul (Dennis), Tsang (Dennis), Ng (Dennis), En Wei, Jiayu Xue
arXiv:2610.07916v1 Announce Type: new
Abstract: Image Manipulation Localization (IML) is commonly formulated as a fully supervised learning task that estimates the optimal manipulation mask $y$ for a...
By Xuekang Zhu, Kaiwen Feng, Ruifeng Wang, Xiwen Wang, Xiaochen Ma, Bo Du, Changjiang Jiang, Chenfan Qu, Songyu Ye, Xia Du, Wentao Feng, Jian Liu, Ji-Zhe Zhou
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.
By Yi Tang, Xinyi Shang, Jiacheng Cui, Sondos Mahmoud Bsharat, Jiacheng Liu, Xiaohan Zhao, Tran Dinh Tien, Ahmed Elhagry, Salwa K. Al Khatib, Tianjun Yao, Yonina C. Eldar, Jing-Hao Xue, Hao Li, Salman Khan, Zhiqiang Shen
BudgetPix introduces a compute‑adaptive tokenization framework for pixel‑space image diffusion, dynamically allocating computational resources based on visual complexity and spatial layout. It comprises an adaptive encoder using an entropy‑guided quadtree and multi‑scale patch embedder, a scale‑aware decoder, and a flexible training schedule that allows pixel‑space denoisers to operate across variable token counts. The method integrates with existing diffusion architectures, achieving comparable fidelity to larger models while using only a fraction of the compute budget, and demonstrates improved quality‑efficiency tradeoffs in both text‑to‑image and class‑conditional generation.
By Ozgur Kara, Yujia Chen, Daniel Watson, David Forsyth, James Matthew Rehg, Wen-Sheng Chu, Du Tran
The paper proposes InterpTok, a method that fine‑tunes the MAETok image tokenizer to better handle codes that lie between training samples. By training the decoder on mixtures of image codes and using a discriminator plus a mixing‑ratio loss, InterpTok preserves reconstruction quality while dramatically improving interpolation and generation metrics. The approach yields sharper, more natural image compositions and significantly lowers FID scores for diffusion models trained on the new tokenizer.
By Ole Bei{\ss}wenger, Jan-Niklas Dihlmann, Hendrik Lensch
arXiv:2608.28706v1 Announce Type: new
Abstract: ViT detectors fix a uniform token grid before any learned stage. A native-resolution aerial detector must then choose between resolving few-pixel objec...
By Khayrul Islam
SpectralCanary introduces a simple method for auditing whether a text‑to‑image model has been fine‑tuned on a specific image collection. It embeds a low‑frequency visual signal by blending a fixed canary image into every dataset image with a smooth low‑pass mask, leaving captions unchanged and avoiding per‑image optimization. A lightweight verifier then detects this signal in a model’s generated outputs, achieving high true‑positive rates across multiple datasets and architectures while preserving image quality.
By Sy-Tuyen Ho, Anirudh Satheesh, Soham Chawathe, Huy Nghiem, Yongkyun Lee, Andrew Adams Schoen, Furong Huang