  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.
