Deepfake Detection

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Deepfake detection studies how to distinguish authentic images, video, and audio from manipulated or AI-generated content, a capability important for verification and security. Detectors look for spatial and frequency artifacts, temporal or physiological irregularities, and inconsistencies across audio, video, and semantic or physical cues; some also use pretrained foundation models, vision-language reasoning, or watermarks. Beyond classifying media, methods increasingly aim to localize altered regions or time spans, identify sources, and provide evidence-grounded explanations. A central challenge is that strong results on familiar benchmarks often fail to transfer to unseen generators, real-world conditions, or degraded media, while shortcuts, calibration, and fairness complicate reliable deployment.

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Band-Attention Modulation Network for Robust Face Forgery Detection
Apr 9, 2024Zhida Zhang, Wenkui Yang, Xinlei Ma +2Image Corruption Robustness Image Forgery Detection
University of Chinese Academy of Sciences, Beijing, China · MAIS & CRIPAC, Institute of Automation, Chinese Academy of Sciences, Beijing, China

Apr 9, 2024J/K move · Enter open · S save

Band-Attention Modulation Network for Robust Face Forgery Detection

Zhida Zhang, Wenkui Yang, Xinlei Ma, Qihang Fan+1

University of Chinese Academy of Sciences, Beijing, China · MAIS & CRIPAC, Institute of Automation, Chinese Academy of Sciences, Beijing, China

Image Corruption Robustness Image Forgery Detection Frequency-Domain Feature Learning Deepfake Detection

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Face forgery detection faces critical challenges in generalizing to unseen manipulation techniques and remaining robust under image compression, which often obscures subtle artifacts. Existing methods typically rely on fixed filters or coarse band separation, lacking the adaptability to learn task-specific spectral cues. To address this, we propose the Band-Attention Modulation Network (BAM-Net), a novel framework that pioneers learnable, fine-grained modulation of frequency components for forgery detection. At its core is the Band-Attention Modulation (BAM) mechanism, which transforms an image into its Discrete Cosine Transform (DCT) spectrogram and learns to dynamically reweight frequency bands along anti-diagonals. This process effectively enhances forgery-related spectral signatures while suppressing less informative ones, simulating an adaptive "inverse compression" that counters information loss. The modulated frequency information is then fused with the spatial domain to guide a lightweight yet effective spatial backbone equipped with distance-decayed attention for comprehensive feature extraction. Extensive experiments on FaceForensics++, Celeb-DF, and DFDC datasets demonstrate that BAM-Net achieves state-of-the-art performance. More importantly, it exhibits exceptional generalization in cross-dataset, cross-compression, and cross-manipulation scenarios, underscoring the vital role of adaptive frequency band modulation in building robust forgery detectors.