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.

采用二进制神经网络（BNNs）进行实时图像深伪造检测，以提高效率并取得较高准确率，在频域和纹理域探测操纵痕迹，性能优于现有方法，并显著减少运算量。

超越谎言：使用二进制神经网络的实时深假识别

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

Designing a novel Local Binary Pattern (LBP) process usually relies heavily
on human experts' knowledge and experience in the area. Even experts are often
left with tedious episodes of trial and error until they identify an optimal
LBP for a particular dataset. To address this problem, we present a novel
symbolic regression able to automatically discover LBP formulas to remove the
moving parts of a scene by segmenting it into a background and a foreground.
Experimental results conducted on real videos of outdoor urban scenes under
various conditions show that the LBPs discovered by the proposed approach
significantly outperform the previous state-of-the-art LBP descriptors both
qualitatively and quantitatively. Our source code and data will be available
online.

通过符号回归自动发现 LBP 公式，进而通过场景分割将一个场景划分为背景和前景，实验证明所提方法发现的 LBPs 在质量和数量上明显优于先前的最先进 LBP 描述符。