Crafting imperceptible and transferable adversarial examples: leveraging conditional residual generator and wavelet transforms to deceive deepfake detection

The malicious abuse of deepfakes has raised serious ethical, security, and privacy concerns, eroding public trust in digital media. While existing deepfake detectors can detect fake images, they are vulnerable to adversarial attacks. Although various adversarial attacks have been explored, most are white-box attacks difficult to realize in practice, and the generated adversarial examples have poor quality easily noticeable to the human eye. For this detection task, the goal should be to generate adversarial examples that can deceive detectors while maintaining high quality and authenticity. We propose a method to generate imperceptible and transferable adversarial examples aimed at fooling unknown deepfake detectors. The method combines a conditional residual generator with an accessible detector as a surrogate model, utilizing the detector's relative distance loss function to generate highly transferable adversarial examples. Discrete wavelet transform is also introduced to enhance image quality. Extensive experiments demonstrate that the adversarial examples generated by our method not only possess excellent visual quality but also effectively deceive various detectors, exhibiting superior cross-detector transferability in black-box attacks. Our code is available at:https://github.com/SiSuiyuHang/ITA.