FaceGuard: Proactive Deepfake Detection
This addresses the challenge of detecting deepfakes for social media users and platforms, offering a proactive approach to counter new generation methods, though it is incremental as it builds on watermarking techniques.
The paper tackles the problem of deepfake detection by proposing FaceGuard, a proactive framework that embeds watermarks into real face images before publication, enabling detection by verifying watermark mismatches, and it shows accurate detection outperforming existing methods on multiple datasets.
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.