Band-Attention Modulated RetNet for Face Forgery Detection
Zhida Zhang Jie Cao Wenkui Yang Qihang Fan Kai Zhou Ran He
The transformer networks are extensively utilized in face forgery detection
due to their scalability across large datasets.Despite their success,
transformers face challenges in balancing the capture of global context, which
is crucial for unveiling forgery clues, with computational complexity.To
mitigate this issue, we introduce Band-Attention modulated RetNet (BAR-Net), a
lightweight network designed to efficiently process extensive visual contexts
while avoiding catastrophic forgetting.Our approach empowers the target token
to perceive global information by assigning differential attention levels to
tokens at varying distances. We implement self-attention along both spatial
axes, thereby maintaining spatial priors and easing the computational
burden.Moreover, we present the adaptive frequency Band-Attention Modulation
mechanism, which treats the entire Discrete Cosine Transform spectrogram as a
series of frequency bands with learnable weights.Together, BAR-Net achieves
favorable performance on several face forgery datasets, outperforming current
state-of-the-art methods.