  The detection of malicious deepfakes is a constantly evolving problem that
requires continuous monitoring of detectors to ensure they can detect image
manipulations generated by the latest emerging models. In this paper, we
investigate the vulnerability of single-image deepfake detectors to black-box
attacks created by the newest generation of generative methods, namely
Denoising Diffusion Models (DDMs). Our experiments are run on FaceForensics++,
a widely used deepfake benchmark consisting of manipulated images generated
with various techniques for face identity swapping and face reenactment.
Attacks are crafted through guided reconstruction of existing deepfakes with a
proposed DDM approach for face restoration. Our findings indicate that
employing just a single denoising diffusion step in the reconstruction process
of a deepfake can significantly reduce the likelihood of detection, all without
introducing any perceptible image modifications. While training detectors using
attack examples demonstrated some effectiveness, it was observed that
discriminators trained on fully diffusion-based deepfakes exhibited limited
generalizability when presented with our attacks.
