This paper presents an interpretable deepfake detection framework that explicitly encodes physically grounded forensic cues to analyze spatially and temporally coherent facial features in video sequences. The method transforms videos into identity-consistent facial trajectories, segments them into fixed-length temporal windows, and represents each frame with 68 structured descriptors across photometric, textural, geometric, and compression domains. These descriptors are processed by an LSTM to capture temporal dependencies, achieving strong F1-scores on four benchmark datasets and demonstrating robust cross-dataset generalization.
By Chahira Benhama, Mohand Sa\"id Allili, Assia Hamadene
arXiv:2610.29442v1 Announce Type: new
Abstract: Recent advances in generative models have enabled highly realistic face forgeries, yet generalizing deepfake detectors to unseen manipulation pipelines...
By Chao Shuai, Shaojing Fan, Kun Pan, Jinwu Hu, Weichen Lian, Ziqi Yang, Zhongjie Ba, Zhenguang Liu
arXiv:2609.12668v1 Announce Type: new
Abstract: Recent deepfake detection studies increasingly suggest remote photoplethysmography (rPPG) signals as an authenticity cue. However, existing benchmarks...
By Chenxi Yang, Yassine Ouzar, Larbi Boubchir
arXiv:2606. 23825v1 Announce Type: cross Abstract: Efficient small object detection is bottlenecked by the inherent feature scarcity of tiny targets, which is further aggravated by operations of spatial-domain detectors that indiscriminately discard critical high-frequency details.
By Yuhan Rui, Shihan Qiao, Yibin Lou, Mingxi Yu, Yutong Wan, Yanqiao Chen, Dongsheng Hou, Zhen Cao, Athena Zhuoming Zhong, Qi Hao
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: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:2608.23363v1 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
arXiv:2603.14005v2 Announce Type: replace
Abstract: To generalize deepfake detectors to future unseen forgeries, most existing methods attempt to simulate the dynamically evolving forgery types using...
By Ming-Hui Liu, Harry Cheng, Xin Luo, Xin-Shun Xu, Mohan S. Kankanhalli
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:2608. 06865v1 Announce Type: cross Abstract: The malicious use of generative artificial intelligence to create highly realistic deepfake videos raises serious ethical concerns and poses substantial challenges to AI safety.
By Xuechao Zou, Shun Zhang, Kai Li, Yi Zhou, Xinyu Sun, Yuhui Chen, Zhe Wu, Congyan Lang, Junliang Xing
arXiv:2502.10920v2 Announce Type: replace-cross
Abstract: Deepfakes, particularly those involving faceswap-based manipulations, have sparked significant societal concern due to their increasing reali...
By Simiao Ren, Hengwei Xu, Tsang Ng, Kidus Zewde, Shengkai Jiang, Ramini Desai, Disha Patil, Ning-Yau Cheng, Yining Zhou, Ragavi Muthukrishnan
The paper introduces Band-Attention Modulation Network (BAM‑Net), a face forgery detection framework that learns fine‑grained, adaptive modulation of frequency bands in the Discrete Cosine Transform spectrogram. BAM‑Net dynamically reweights anti‑diagonal frequency bands to enhance forgery‑related spectral cues while suppressing irrelevant information, then fuses this modulated frequency data with spatial features using a lightweight backbone with distance‑decayed attention. Experiments on FaceForensics++, Celeb‑DF, and DFDC show that BAM‑Net achieves state‑of‑the‑art performance and strong generalization across datasets, compression levels, and manipulation types.
By Zhida Zhang, Wenkui Yang, Xinlei Ma, Qihang Fan, Jie Cao