The paper introduces AdaGate-DF, an adaptive gated deepfake detection framework designed for low-resolution and resource-constrained environments. By leveraging image-quality cues, the system routes samples through a dual multi-exit architecture, allowing high-quality images to exit earlier and reduce computational load. Evaluations on Celeb-DF and FaceForensics++ show that AdaGate-DF outperforms existing models such as MaD-CoRN and DefakeHop++ while maintaining low inference latency and robust performance across varying resolutions and class imbalance scenarios.
By Vaishnavi Sen, Cody Laurie, Rashida Hasan
arXiv:2411. 19537v2 Announce Type: replace-cross Abstract: We survey deepfake generation and detection techniques, covering all deepfake media types: image, video, audio and multimodal content.
By Florinel-Alin Croitoru, Andrei-Iulian Hiji, Vlad Hondru, Nicolae Catalin Ristea, Paul Irofti, Marius Popescu, Cristian Rusu, Radu Tudor Ionescu, Fahad Shahbaz Khan, Mubarak Shah
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:2608. 03096v1 Announce Type: cross Abstract: Recent advances in video generation models have significantly intensified the deepfake threat, yet the current deepfake video detection benchmarks remain underdeveloped.
By Pei Li, Sihan Chen, Delong Ran, Tianshuo Cong
arXiv:2609.23830v1 Announce Type: new
Abstract: Comparing audio-visual deepfake detectors requires coordinating dataset adaptation, temporal input representation, model interfaces and experimental co...
By Jan Rybarczyk, Mateusz Roszkowski, Jacek Komorowski
Recent advances in video generation models have significantly intensified the deepfake threat, yet the current deepfake video detection benchmarks remain underdeveloped. In particular, the effectiveness of image-level detectors in the video domain has not been systematically assessed.
arXiv:2609.14437v1 Announce Type: cross
Abstract: Deepfake detection systems often exhibit significant performance degradation when deployed on unseen manipulation methods, limiting their reliability...
By Arya Pulkit, Aditya Ruhela, Akarshan Kapoor, Arnav Bhavsar
arXiv:2509.24367v2 Announce Type: replace
Abstract: Deepfake generators evolve rapidly, making exhaustive data collection and repeated retraining impractical. Unlike generic multi-task settings, deep...
By Jinhee Park, Guisik Kim, Choongsang Cho, Junseok Kwon
arXiv:2608.22368v1 Announce Type: new
Abstract: While linear attention is a compelling mechanism for high-resolution object detection due to its reduced cost for global token mixing, converting the S...
By Huaiyuan Qin, Gabriel James Goenawan, Zihang Lin, Muli Yang, Hongyuan Zhu
Explainable Deepfake Detection with Feature-robust Augmentation and Evidence-grounded Explanation Optimization proposes a new framework that improves deepfake detection and interpretability. The approach introduces Feature-robust Augmentation—diversified degradation-aware strategies combined with supervised contrastive learning and a mean-teacher architecture—to maintain accuracy on low-quality images. For explanations, it employs evidence-grounded preference optimization, guiding the model to focus on genuine manipulation traces by learning from chosen-rejected explanation pairs that omit evidence or inject irrelevant details. The method achieved first place in the ACM Multimedia 2026 Explainable Deepfake Detection Challenge and is publicly available on GitHub.
By Zhu Xu, Jiaqi Tang, Pokai Chen, Yuxin Peng, Yang Liu
arXiv:2608.23363v1 Announce Type: cross
Abstract: Audio-visual deepfake detection is an actively studied topic, where one of the main challenges is to develop detectors able to generalize across deep...
By Vlad Hondru, Florinel Alin Croitoru, Iuliana Georgescu, A. Sophia Koepke, Radu Tudor Ionescu
OD3 introduces an optimization‑free dataset distillation framework tailored for object detection. The method first iteratively places object instances in synthesized images, then screens candidates with a pre‑trained observer model to discard low‑confidence objects. Applied to MS COCO and PASCAL VOC, OD3 achieves compression ratios from 0.25% to 5% and surpasses previous detection‑focused distillation methods by over 14% on COCO mAP50 at a 1.0% compression ratio.
By Salwa K. Al Khatib, Ahmed ElHagry, Shitong Shao, Zhiqiang Shen