Frequency-Aware Deepfake Detection: Improving Generalizability through Frequency Space Domain Learning

This research addresses the challenge of developing a universal deepfake detector that can effectively identify unseen deepfake images despite limited training data. Existing frequency-based paradigms have relied on frequency-level artifacts introduced during the up-sampling in GAN pipelines to detect forgeries. However, the rapid advancements in synthesis technology have led to specific artifacts for each generation model. Consequently, these detectors have exhibited a lack of proficiency in learning the frequency domain and tend to overfit to the artifacts present in the training data, leading to suboptimal performance on unseen sources. To address this issue, we introduce a novel frequency-aware approach called FreqNet, centered around frequency domain learning, specifically designed to enhance the generalizability of deepfake detectors. Our method forces the detector to continuously focus on high-frequency information, exploiting high-frequency representation of features across spatial and channel dimensions. Additionally, we incorporate a straightforward frequency domain learning module to learn source-agnostic features. It involves convolutional layers applied to both the phase spectrum and amplitude spectrum between the Fast Fourier Transform (FFT) and Inverse Fast Fourier Transform (iFFT). Extensive experimentation involving 17 GANs demonstrates the effectiveness of our proposed method, showcasing state-of-the-art performance (+9.8\%) while requiring fewer parameters. The code is available at https://github.com/chuangchuangtan/FreqNet-DeepfakeDetection.

Authors

• Chuangchuang Tan Institute of Information Science, Beijing Jiaotong University Visual Intellgence +X International Cooperation Joint Laboratory of MOE
• Yao Zhao Institute of Information Science, Beijing Jiaotong University Beijing Key Laboratory of Advanced Information Science and Network Technology
• Shikui Wei Institute of Information Science, Beijing Jiaotong University, China Beijing Key Laboratory of Advanced Information Science and Network Technology
• Guanghua Gu School of Information Science and Engineering, Yanshan University Hebei Key Laboratory of Information Transmission and Signal Processing
• Ping Liu Center for Frontier AI Research, IHPC, A*STAR, Singapore
• Yunchao Wei Institute of Information Science, Beijing Jiaotong University Beijing Key Laboratory of Advanced Information Science and Network Technology

Keywords

• CV: Other Foundations of Computer Vision
• CV: Scene Analysis & Understanding