  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 {\cred
\url{https://github.com/chuangchuangtan/FreqNet-DeepfakeDetection}}.
