Frequency Bias Matters: Diving Into Robust and Generalized Deep Image Forgery Detection

Summary

Researchers study why deep image forgery detectors fail to generalize to unseen GANs and to noisy samples. They trace both problems to a frequency bias in DNN detectors and propose a two-step frequency alignment method that removes the frequency discrepancy between real and fake images. The method works as a black-box attack on detectors or as a universal defense, tested across twelve detectors, eight forgery models and five metrics.

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