  Existing deepfake-detection methods focus on passive detection, i.e., they
detect fake face images via exploiting the artifacts produced during deepfake
manipulation. A key limitation of passive detection is that it cannot detect
fake faces that are generated by new deepfake generation methods. In this work,
we propose FaceGuard, a proactive deepfake-detection framework. FaceGuard
embeds a watermark into a real face image before it is published on social
media. Given a face image that claims to be an individual (e.g., Nicolas Cage),
FaceGuard extracts a watermark from it and predicts the face image to be fake
if the extracted watermark does not match well with the individual's ground
truth one. A key component of FaceGuard is a new deep-learning-based
watermarking method, which is 1) robust to normal image post-processing such as
JPEG compression, Gaussian blurring, cropping, and resizing, but 2) fragile to
deepfake manipulation. Our evaluation on multiple datasets shows that FaceGuard
can detect deepfakes accurately and outperforms existing methods.
