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

TL;DR

This paper introduces a real-time deepfake detection method using Binary Neural Networks combined with FFT and LBP features, achieving high efficiency with minimal accuracy loss and state-of-the-art performance on multiple datasets.

Contribution

The paper presents a novel deepfake detection approach employing Binary Neural Networks with frequency and texture features for fast inference and improved efficiency.

Findings

Achieves up to 20x reduction in FLOPs during inference.

Demonstrates state-of-the-art detection performance on multiple datasets.

Balances accuracy and efficiency using BNNs in deepfake detection.

Abstract

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.