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…

• NS
  National Science Foundation
• NN
  National Natural Science Foundation of China

  Awards: 62072394, 62120106009, U1936212
• NS
  Natural Science Foundation of Hebei Province

  Award: F2021203019