Turn Fake into Real: Adversarial Head Turn Attacks Against Deepfake
  Detection
Weijie Wang Zhengyu Zhao Nicu Sebe Bruno Lepri
Malicious use of deepfakes leads to serious public concerns and reduces
people's trust in digital media. Although effective deepfake detectors have
been proposed, they are substantially vulnerable to adversarial attacks. To
evaluate the detector's robustness, recent studies have explored various
attacks. However, all existing attacks are limited to 2D image perturbations,
which are hard to translate into real-world facial changes. In this paper, we
propose adversarial head turn (AdvHeat), the first attempt at 3D adversarial
face views against deepfake detectors, based on face view synthesis from a
single-view fake image. Extensive experiments validate the vulnerability of
various detectors to AdvHeat in realistic, black-box scenarios. For example,
AdvHeat based on a simple random search yields a high attack success rate of
96.8% with 360 searching steps. When additional query access is allowed, we can
further reduce the step budget to 50. Additional analyses demonstrate that
AdvHeat is better than conventional attacks on both the cross-detector
transferability and robustness to defenses. The adversarial images generated by
AdvHeat are also shown to have natural looks. Our code, including that for
generating a multi-view dataset consisting of 360 synthetic views for each of
1000 IDs from FaceForensics++, is available at
https://github.com/twowwj/AdvHeaT.