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
The paper introduces a video forgery detection system that fuses spatial and frequency-domain features using a ResNet‑LSTM backbone with a Convolutional Block Attention Module (CBAM) and a Discrete Cosine Transform (DCT) module. Experiments on multiple benchmark datasets show that this hybrid architecture outperforms existing methods in distinguishing authentic from manipulated videos. Ablation and comparative studies confirm the individual contributions of each component, highlighting the model’s effectiveness across diverse forgery scenarios.
By Zihao Liao, Sheng Hong, Yu Chen
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: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: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
The paper introduces Modality‑Specific Frequency Distillation (MSFD), a continual learning framework for video deepfake detection that separates spatial, temporal, and spatiotemporal features in the frequency domain. By preserving each modality independently and applying a cross‑modality decorrelation loss, MSFD adapts to new forgery patterns while maintaining performance across diverse continual deepfake video scenarios. Experiments demonstrate that this approach outperforms state‑of‑the‑art methods in both adaptation and retention.
By Taehoon Kim, Jongwook Choi, Heejae Jo, Byungmin Park, Jongwon Choi