Exposing DeepFake Videos Using Attention Based Convolutional LSTM Network

The detection of face tampering in videos created by artificial intelligence techniques (commonly known as the Deep Fakes) has become an important and challenging task in network security defense. In this paper, we propose a novel attention-based deep fake video detection method, which captures the sharp changes in terms of the facial features caused by the composite video. We utilize the convolutional long short-term memory to extract both spatial and temporal information of DeeFake videos. Meanwhile, we apply the attention mechanism to emphasize the specific facial area of each video frame. Finally, we design a decoder to further fusion multiple frames information for more accurate detection results. Experimental results and comparisons with state-of-the-art methods demonstrate that our framework achieves superior performance.