Faster Than Lies: Real-time Deepfake Detection using Binary Neural Networks

Deepfake detection aims to contrast the spread of deep-generated media that undermines trust in online content. While existing methods focus on large and complex models the need for real-time detection demands greater efficiency. With this in mind unlike previous work we introduce a novel deepfake detection approach on images using Binary Neural Networks (BNNs) for fast inference with minimal accuracy loss. Moreover our method incorporates Fast Fourier Transform (FFT) and Local Binary Pattern (LBP) as additional channel features to uncover manipulation traces in frequency and texture domains. Evaluations on COCOFake DFFD and CIFAKE datasets demonstrate our method's state-of-the-art performance in most scenarios with a significant efficiency gain of up to a 20xreduction in FLOPs during inference. Finally by exploring BNNs in deepfake detection to balance accuracy and efficiency this work paves the way for future research on efficient deepfake detection.