DiffFake: Exposing Deepfakes using Differential Anomaly Detection
This addresses the challenge of detecting deepfakes generated by unseen techniques, which is crucial for security and media integrity, though it is an incremental improvement over existing anomaly detection approaches.
The paper tackles the problem of deepfake detection by proposing DiffFake, a method that treats detection as an anomaly detection task using differential analysis of facial images, and it shows competitive or superior performance compared to state-of-the-art methods on five datasets.
Traditional deepfake detectors have dealt with the detection problem as a binary classification task. This approach can achieve satisfactory results in cases where samples of a given deepfake generation technique have been seen during training, but can easily fail with deepfakes generated by other techniques. In this paper, we propose DiffFake, a novel deepfake detector that approaches the detection problem as an anomaly detection task. Specifically, DiffFake learns natural changes that occur between two facial images of the same person by leveraging a differential anomaly detection framework. This is done by combining pairs of deep face embeddings and using them to train an anomaly detection model. We further propose to train a feature extractor on pseudo-deepfakes with global and local artifacts, to extract meaningful and generalizable features that can then be used to train the anomaly detection model. We perform extensive experiments on five different deepfake datasets and show that our method can match and sometimes even exceed the performance of state-of-the-art competitors.