用人脸特征点检测深度伪造，避开图像处理，效果更优。
Deepfake Detection Via Facial Feature Extraction and Modeling
- 提取人脸关键点，分析面部动作细微异常。
- RNN和ANN准确率达93%-96%，CNN约78%。
- 方法轻量高效，适合实际应用部署。
深度伪造技术的兴起引发了对在线媒体真实性的新质疑。由人工智能生成的视频和图像越来越难以与真实内容区分，亟需新型检测模型。尽管已有多种方法尝试解决此问题，但多数依赖直接图像处理，使用卷积神经网络（CNN）或循环神经网络（RNN）对视频帧进行分析。本文提出一种仅基于人脸关键点的深度伪造检测方法，利用包含真实与伪造人脸视频的数据集，提取面部关键点以识别面部运动中的微小不一致，而非依赖原始图像。实验表明，该特征提取方法在三种神经网络模型中均表现良好：RNN与人工神经网络（ANN）准确率分别达96%和93%，而CNN模型准确率约为78%。本研究挑战了“必须进行原始图像处理”的传统假设，证明基于面部特征的提取方法可兼容多种模型且参数更少，具有实际应用潜力。
原文摘要 · Abstract (English)
The rise of deepfake technology brings forth new questions about the authenticity of various forms of media found online today. Videos and images generated by artificial intelligence (AI) have become increasingly more difficult to differentiate from genuine media, resulting in the need for new models to detect artificially-generated media. While many models have attempted to solve this, most focus on direct image processing, adapting a convolutional neural network (CNN) or a recurrent neural network (RNN) that directly interacts with the video image data. This paper introduces an approach of using solely facial landmarks for deepfake detection. Using a dataset consisting of both deepfake and genuine videos of human faces, this paper describes an approach for extracting facial landmarks for deepfake detection, focusing on identifying subtle inconsistencies in facial movements instead of raw image processing. Experimental results demonstrated that this feature extraction technique is effective in various neural network models, with the same facial landmarks tested on three neural network models, with promising performance metrics indicating its potential for real-world applications. The findings discussed in this paper include RNN and artificial neural network (ANN) models with accuracy between 96% and 93%, respectively, with a CNN model hovering around 78%. This research challenges the assumption that raw image processing is necessary to identify deepfake videos by presenting a facial feature extraction approach compatible with various neural network models while requiring fewer parameters.
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