Dynamic Difference Learning With Spatio–Temporal Correlation for Deepfake Video Detection

With the rapid development of face forgery techniques, the existing frame-based deepfake video detection methods have fell into a dilemma that frame-based methods may fail when encountering extremely realistic images. To overcome the above problem, many approaches attempted to model the spatio-temporal inconsistency of videos to distinguish real and fake videos. However, current works model spatio-temporal inconsistency by combining intra-frame and inter-frame information, but ignore the disturbance caused by facial motions that would limit further improvement in detection performance. To address this issue, we investigate into long and short range inter-frame motions and propose a novel dynamic difference learning method to distinguish between the inter-frame differences caused by face manipulation and the inter-frame differences caused by facial motions in order to model precise spatio-temporal inconsistency for deepfake video detection. Moreover, we elaborately design a dynamic fine-grained difference capture module (DFDC-module) and a multi-scale spatio-temporal aggregation module (MSA-module) to collaboratively model spatio-temporal inconsistency. Specifically, the DFDC-module applies self-attention mechanism and fine-grained denoising operation to eliminate the differences caused by facial motions and generates long range difference attention maps. The MSA-module is devised to aggregate multi-direction and multi-scale temporal information to model spatio-temporal inconsistency. The existing 2D CNNs can be extended into dynamic spatio-temporal inconsistency capture networks by integrating the proposed two modules. Extensive experimental results demonstrate that our proposed algorithm steadily outperforms state-of-the-art methods by a clear margin in different benchmark datasets.