论文标题
时间依赖性在DeepFake视频检测中的有效性
The Effectiveness of Temporal Dependency in Deepfake Video Detection
论文作者
论文摘要
Deepfakes是一种用于生成恶意目的的个人的伪造视频的合成图像产生形式。最终的视频可用于传播错误信息，减少对媒体的信任或作为勒索形式。这些威胁需要自动摄影视频检测方法。本文研究了时间信息是否可以改善深度学习模型的深层检测性能。 为了调查这一点，我们提出了一个框架，该框架通过其定义特征对新的和现有的方法进行了分类。这些是特征提取的类型：自动或手动，以及框架之间的时间关系：依赖或独立。我们应用此框架来研究时间依赖性对模型的深膜检测性能的影响。 我们发现，时间依赖性在使用自动特征选择为模型分类的真实图像时会产生具有统计学意义的（p <0.05），这表明时空信息可以提高DeepFake视频检测模型的性能。
Deepfakes are a form of synthetic image generation used to generate fake videos of individuals for malicious purposes. The resulting videos may be used to spread misinformation, reduce trust in media, or as a form of blackmail. These threats necessitate automated methods of deepfake video detection. This paper investigates whether temporal information can improve the deepfake detection performance of deep learning models. To investigate this, we propose a framework that classifies new and existing approaches by their defining characteristics. These are the types of feature extraction: automatic or manual, and the temporal relationship between frames: dependent or independent. We apply this framework to investigate the effect of temporal dependency on a model's deepfake detection performance. We find that temporal dependency produces a statistically significant (p < 0.05) increase in performance in classifying real images for the model using automatic feature selection, demonstrating that spatio-temporal information can increase the performance of deepfake video detection models.
