Spectra is a deepfake detection framework that uses implicit neural representations (INRs) to analyze how an image is reconstructed, producing multi-level reconstruction heatmaps. These heatmaps are fused with semantic visual features via a bidirectional mechanism, enabling the model to detect forgery cues that are less tied to specific generation pipelines. Experiments on several deepfake benchmarks show that Spectra outperforms detectors relying only on RGB appearance or semantic representations, and it also extends to face presentation attack detection.
By Dhananjaya Jayasundara, Kartik Narayan, Vishal M. Patel
arXiv:2510. 05740v2 Announce Type: replace-cross Abstract: The rapid development of generative models has made it increasingly crucial to develop detectors that can reliably detect synthetic images.
By Amirtaha Amanzadi, Zahra Dehghanian, Hamid Beigy, Hamid R. Rabiee
The paper introduces MoE-JEPA, a dual‑stream deepfake detection model that combines a V‑JEPA backbone with a Residual Mixture‑of‑Experts mechanism and a noise stream branch. It further incorporates a Gated Attention Multiple Instance Learning module to refine spatial semantic understanding. On the SID‑Set benchmark, MoE‑JEPA achieves a new state‑of‑the‑art accuracy of 95.54%, outperforming much larger models.
By Simone Teglia, Irene Amerini
The paper introduces FADNet, a face authenticity detector that treats forgery detection as a one‑class classification problem, training only on authentic faces. It uses Evidential Deep Learning to measure uncertainty and a pseudo‑forgery generator to sharpen decision boundaries. Experiments on DF40 and ASFD show FADNet surpasses state‑of‑the‑art methods with 96.63% accuracy and 98.83% precision.
By Qingchao Jiang, Zhenxuan Hou, Zhiying Zhu, Zhenxing Qian, Xinpeng Zhang, Zaiwang Gu
DINO-FIL is a transformer-based framework that localizes generative image forgeries at the pixel level. It fuses multi‑layer representations from a pretrained DINOv3 ViT‑H+/16 encoder with a lightweight decoder to detect both semantic and fine‑grained texture inconsistencies. In cross‑dataset experiments, DINO‑FIL outperforms existing baselines on most datasets while preserving a high true‑negative rate for pristine images.
By Simon Freitter, David Fischinger, Silvia Poletti, Marcel Hasenbalg, Martin Boyer
arXiv:2606. 00101v1 Announce Type: cross Abstract: With the rapid advancement of artificial intelligence generated content (AIGC) technologies, video forgery has become increasingly prevalent, posing new challenges to public discourse and societal security.
By Huidong Feng, Wentao Chen, Jie Chen, Xinqi Cai, Ruolong Ma, Yinglin Zheng, Yuxin Lin, Ming Zeng
arXiv:2604. 17376v2 Announce Type: replace-cross Abstract: In today's day and age, we face a challenge in detecting deepfake images because of the fast evolution of modern generative models and the poor generalization capability of existing methods.
By Kaliki V Srinanda, M Manvith Prabhu, Hemanth K Mogilipalem, Jayavarapu S Abhinai, Vaibhav Santhosh, Aryan Herur, Deepu Vijayasenan
arXiv:2609.14316v1 Announce Type: new
Abstract: Advances in image generation have made synthetic images increasingly difficult to distinguish from real photographs, raising concerns about the trustwo...
By Manni Cui, Ruiqi Liu, Zijian Yu, Hao Tan, Zibo Wei, Zian Wang, Ziheng Qin, Huijia Zhu, Weiqiang Wang, Jun Lan, Shu Wu
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.
By Zhida Zhang, Xinlei Ma, Jie Cao
arXiv:2610.01544v1 Announce Type: new
Abstract: Existing image forgery detectors often suffer from generalization to unseen manipulation methods due to the limited ability to capture transferable for...
By Bingjian Yang, Shilei Zhao, Zheng Wang
Generalizable AI-generated image (AIGI) and deepfake detection remain challenging due to the asymmetry phenomenon, where adaptation to limited training distributions can degrade the transferable repre...
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...
By Baoke Dou, Ziye Wang, Hao Wang, Guoqing Cai, Wende Tan, Chenyang Si, Liucheng Guo, Yueming Lyu