Key frame-based CNN-LSTM model for deepfake video detection
Yan Cheng · Journal of Electronic Imaging · 2025
Deep learning algorithms are extensively employed in the detection of facial forgery. The foundation of these methods relies on the utilization of a large number of frames for learning purposes. In contrast to other state-of-the-art methods that require numerous consecutive or uniformly sampled frames as network input, we propose a key frame strategy to maximize inter-frame dissimilarities. We utilize the weighted energy of optical flow to identify key frames in the video, thereby enhancing the quality of features through CNN-LSTM training. Evaluations conducted on FaceForensics++, Celeb-DF, and DFDC datasets demonstrate that the proposed method achieves superior or comparable performance in terms of within and cross dataset testing.