Counterfactual Explanations for Face Forgery Detection via Adversarial
  Removal of Artifacts
Yang Li Songlin Yang Wei Wang Ziwen He Bo Peng Jing Dong
Highly realistic AI generated face forgeries known as deepfakes have raised
serious social concerns. Although DNN-based face forgery detection models have
achieved good performance, they are vulnerable to latest generative methods
that have less forgery traces and adversarial attacks. This limitation of
generalization and robustness hinders the credibility of detection results and
requires more explanations. In this work, we provide counterfactual
explanations for face forgery detection from an artifact removal perspective.
Specifically, we first invert the forgery images into the StyleGAN latent
space, and then adversarially optimize their latent representations with the
discrimination supervision from the target detection model. We verify the
effectiveness of the proposed explanations from two aspects: (1) Counterfactual
Trace Visualization: the enhanced forgery images are useful to reveal artifacts
by visually contrasting the original images and two different visualization
methods; (2) Transferable Adversarial Attacks: the adversarial forgery images
generated by attacking the detection model are able to mislead other detection
models, implying the removed artifacts are general. Extensive experiments
demonstrate that our method achieves over 90% attack success rate and superior
attack transferability. Compared with naive adversarial noise methods, our
method adopts both generative and discriminative model priors, and optimize the
latent representations in a synthesis-by-analysis way, which forces the search
of counterfactual explanations on the natural face manifold. Thus, more general
counterfactual traces can be found and better adversarial attack
transferability can be achieved.