Key points are not available for this paper at this time.
As deepfake techniques become more sophisticated, the demand for fake facial image detection continues to increase. Various deepfake detection techniques have been introduced but detecting all types of deepfake images with a single model remains challenging.We propose a technique for detecting various types of deepfake images using three common traces generated by deepfakes: residual noise, warping artifacts, and blur effects. We adopted a network designed for steganalysis to detect pixel-wise residual-noise traces. We also consider landmarks, which are the primary parts of the face where unnatural deformations often occur in deepfake images, to capture high-level features. Finally, because the effect of a deepfake is similar to that of blurring, we apply features from various image quality measurement tools that can capture traces of blurring. The results demonstrate that each detection strategy is efficient, and that the performance of the proposed network is stable and superior to that of existing detection networks on datasets of various deepfake types.
Kang et al. (Sat,) studied this question.