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

The evolving sophistication and intricacies of Large Language Models (LLMs)
yield unprecedented advancements, yet they simultaneously demand considerable
computational resources and incur significant costs. To alleviate these
challenges, this paper introduces a novel, simple, and effective method named
``\growlength'' to accelerate the pretraining process of LLMs. Our method
progressively increases the training length throughout the pretraining phase,
thereby mitigating computational costs and enhancing efficiency. For instance,
it begins with a sequence length of 128 and progressively extends to 4096. This
approach enables models to process a larger number of tokens within limited
time frames, potentially boosting their performance. In other words, the
efficiency gain is derived from training with shorter sequences optimizing the
utilization of resources. Our extensive experiments with various
state-of-the-art LLMs have revealed that models trained using our method not
only converge more swiftly but also exhibit superior performance metrics
compared to those trained with existing methods. Furthermore, our method for
LLMs pretraining acceleration does not require any additional engineering
efforts, making it a practical solution in the realm of LLMs.

通过增加训练长度加速大型语言模型 (LLL) 的预训练过程，从而提高效率、减少计算成本，并改善性能。