Low-Quality Deepfake Detection via Unseen Artifacts

The proliferation of manipulated media over the Internet has become a major source of concern in recent times. With the wide variety of techniques being used to create fake media, it has become increasingly difficult to identify such occurrences. While existing algorithms perform well on the detection of such media, limited algorithms take the impact of compression into account. Different social media platforms use different compression factors and algorithms before sharing such images and videos, which amplifies the issues in their identification. Therefore, it has become imperative that fake media detection algorithms work well for data compressed at different factors. To this end, the focus of this article is detecting low-quality fake videos in the compressed domain. The proposed algorithm distinguishes real images and videos from altered ones by using a learned visibility matrix, which enforces the model to see unseen imperceptible artifacts in the data. As a result, the learned model is robust to loss of information due to data compression. The performance is evaluated on three publicly available datasets, namely Celeb-DF, FaceForensics, and FaceForensics++, with three manipulation techniques, viz., Deepfakes, Face2Face, and FaceSwap. Experimental results show that the proposed approach is robust under different compression factors and yields state-of-the-art performance on the FaceForensics++ and Celeb-DF datasets with 97.14% classification accuracy and 74.45% area under the curve, respectively.