Generalizing Face Forgery Detection with High-frequency Features

By Yucheng Luo, Yong Zhang, Junchi Yan, Wei Liu. Computer Vision and Pattern Recognition, 2021

592 citations recorded by the source corpus.

Current face forgery detection methods achieve high accuracy under the within-database scenario where training and testing forgeries are synthesized by the same algorithm. However, few of them gain satisfying performance under the cross-database scenario where training and testing forgeries are synthesized by different algorithms. In this paper, we find that current CNN-based detectors tend to overfit to method-specific color textures and thus fail to generalize. Observing that image noises remove color textures and expose discrepancies between authentic and tampered regions, we propose to utilize the high-frequency noises for face forgery detection. We carefully devise three functional modules to take full advantage of the high-frequency features. The first is the multi-scale high-frequency feature extraction module that extracts high-frequency noises at multiple scales and composes a novel modality. The second is the residual-guided spatial attention module that guides the low-level RGB feature extractor to concentrate more on forgery traces from a new perspective. The last is the cross-modality attention module that leverages the correlation between the two complementary modalities to promote feature learning for each other. Comprehensive evaluations on several benchmark databases corroborate the superior generalization performance of our proposed method.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/fc4defdfb3410bc0ebda8ffc7d7a9258519c6cd8

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Citations in the corpus

Self-supervised Learning of Adversarial Example: Towards Good Generalizations for Deepfake Detection (2022, methodology): “To ensure fair comparisons, we use the provided codes of Xception [41], RFM [47], and SRM [30] from the authors, and we reimplement Face X-ray [24] and F3Net [39] rigorously following the companion paper’s instructions and train these models under the same settings.”
Frequency-Aware Deepfake Detection: Improving Generalizability through Frequency Space Domain Learning (2024, background): “High-Frequency Representation of Images High-frequency artifacts have been recognized as valuable indicators for distinguishing between real and fake images (Qian et al. 2020; Luo et al. 2021).”
UCF: Uncovering Common Features for Generalizable Deepfake Detection (2023, methodology): “To assess the generalization capacity of our framework, we reproduce ten competing methods under consistent conditions for a comprehensive comparison: Xception [37], Face X-ray [23], F3Net [36], SRM [30], SPSL [28], RECCE [6], CORE [35] , SLADD [7], and Liang et al. [27].”
Detecting Deepfakes with Self-Blended Images (2022, background): “Here, we compare our method with other frame-level methods [13, 39, 41, 44, 65].”