End-to-End Reconstruction-Classification Learning for Face Forgery Detection
By Junyi Cao, Chao Ma, Taiping Yao, Shen Chen, Shouhong Ding, Xiaokang Yang. Computer Vision and Pattern Recognition, 2022
420 citations recorded by the source corpus.
Existing face forgery detectors mainly focus on specific forgery patterns like noise characteristics, local textures, or frequency statistics for forgery detection. This causes specialization of learned representations to known forgery patterns presented in the training set, and makes it difficult to detect forgeries with unknown patterns. In this paper, from a new perspective, we propose a forgery detection frame-work emphasizing the common compact representations of genuine faces based on reconstruction-classification learning. Reconstruction learning over real images enhances the learned representations to be aware of forgery patterns that are even unknown, while classification learning takes the charge of mining the essential discrepancy between real and fake images, facilitating the understanding of forgeries. To achieve better representations, instead of only using the encoder in reconstruction learning, we build bipartite graphs over the encoder and decoder features in a multi-scale fashion. We further exploit the reconstruction difference as guidance of forgery traces on the graph output as the final representation, which is fed into the classifier for forgery detection. The reconstruction and classification learning is optimized end-to-end. Extensive experiments on large-scale benchmark datasets demonstrate the superiority of the proposed method over state of the arts.
Subfields: Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/1537faa9593d0d20e62055e4e6af6d382a26d313
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In Computer Vision and Pattern Recognition
Citations in the corpus
- 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].”
- Frequency-Aware Deepfake Detection: Improving Generalizability through Frequency Space Domain Learning (2024, methodology): “Furthermore, various methods (Wang et al. 2020, 2021; Chen et al. 2022; Cao et al. 2022; He et al. 2021; Shiohara et al. 2022)”