Spatial-Phase Shallow Learning: Rethinking Face Forgery Detection in Frequency Domain

By Honggu Liu, Xiaodan Li, Wenbo Zhou, YueFeng Chen, Yuan He, Hui Xue, Weiming Zhang, Nenghai Yu. Computer Vision and Pattern Recognition, 2021

586 citations recorded by the source corpus.

The remarkable success in face forgery techniques has received considerable attention in computer vision due to security concerns. We observe that up-sampling is a necessary step of most face forgery techniques, and cumulative up-sampling will result in obvious changes in the frequency domain, especially in the phase spectrum. According to the property of natural images, the phase spectrum preserves abundant frequency components that provide extra information and complement the loss of the amplitude spectrum. To this end, we present a novel Spatial-Phase Shallow Learning (SPSL) method, which combines spatial image and phase spectrum to capture the up-sampling artifacts of face forgery to improve the transferability, for face forgery detection. And we also theoretically analyze the validity of utilizing the phase spectrum. Moreover, we notice that local texture information is more crucial than high-level semantic information for the face forgery detection task. So we reduce the receptive fields by shallowing the network to suppress high-level features and focus on the local region. Extensive experiments show that SPSL can achieve the state-of-the-art performance on cross-datasets evaluation as well as multi-class classification and obtain comparable results on single dataset evaluation.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/45187881878cd6c21b944312b77e62cefed46b39

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In Computer Vision and Pattern Recognition

1. Baoyuan Wu
2. Toshihiko Yamasaki
3. Zhiyuan Yan
4. Kaede Shiohara
5. Yanbo Fan
6. Yong Zhang

Citations in the corpus

• UCF: Uncovering Common Features for Generalizable Deepfake Detection (2023, methodology): “These works typically utilize blending artifacts [23, 41] or frequency artifacts [28, 30], and some employ adversarial training to synthesize challenging forgeries [7].”
• Detecting Deepfakes with Self-Blended Images (2022, background): “Some studies [23, 37, 43, 44, 50] focus on the frequency domain to capture forgery traces more effectively.”
• Generalized Out-of-Distribution Detection: A Survey (2021, unknown): “4: Frequency-based Methods [227], [228], [229], [230]”