Face-swap Deepfake Detection Using High-frequency Components

Over the last years, very deceitful deepfakes applied to human visuals appeared on theInternet. Given their impressive visual quality, they are nowadays considered a potentialthreat for both individuals and organizations. Hence, researchers started investigatingthe flaws of deepfakes to develop automated tools capable of detecting forged content. Asa result, a wide range of deepfake detection methods has been introduced. In particular,deep learning based-approaches have shown impressive performance. Nevertheless, thesemethods are still not sufficiently robust as they usually consider only one type of artifact,either in the spatial or the frequency domain. In this context, this thesis proposes toleverage the high-frequency components extracted from color images jointly with theoriginal color information to detect unusual traces. It introduces an end-to-end trainablemodule that (a) extracts features from precomputed high-frequency components and (b)fuses them with RGB features. The deepfake detection framework not only exploitsthe high-frequency anomalies present in manipulated images but can also be integratedwith the majority of RGB-based deepfake detectors. Experimental results show thatthe proposed approach improves the performance of state-of-the-art networks, such asXceptionNet and EfficientNet, on a challenging deepfake dataset called Celeb-DF.