Faster Than Lies: Real-time Deepfake Detection using Binary Neural Networks
This addresses the need for efficient detection of deepfakes to combat misinformation online, representing an incremental improvement in balancing accuracy and speed.
The paper tackles the problem of real-time deepfake detection by introducing a method using Binary Neural Networks (BNNs) with FFT and LBP features, achieving state-of-the-art performance on multiple datasets with up to a 20x reduction in FLOPs during inference.
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 $20\times$ reduction 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.