Deepfake Detection without Deepfakes: Generalization via Synthetic
  Frequency Patterns Injection
Davide Alessandro Coccomini Roberto Caldelli Claudio Gennaro Giuseppe Fiameni Giuseppe Amato Fabrizio Falchi
Deepfake detectors are typically trained on large sets of pristine and
generated images, resulting in limited generalization capacity; they excel at
identifying deepfakes created through methods encountered during training but
struggle with those generated by unknown techniques. This paper introduces a
learning approach aimed at significantly enhancing the generalization
capabilities of deepfake detectors. Our method takes inspiration from the
unique "fingerprints" that image generation processes consistently introduce
into the frequency domain. These fingerprints manifest as structured and
distinctly recognizable frequency patterns. We propose to train detectors using
only pristine images injecting in part of them crafted frequency patterns,
simulating the effects of various deepfake generation techniques without being
specific to any. These synthetic patterns are based on generic shapes, grids,
or auras. We evaluated our approach using diverse architectures across 25
different generation methods. The models trained with our approach were able to
perform state-of-the-art deepfake detection, demonstrating also superior
generalization capabilities in comparison with previous methods. Indeed, they
are untied to any specific generation technique and can effectively identify
deepfakes regardless of how they were made.