Abstract
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.
Abstract (translated)
深度伪造技术的兴起引发了关于当今在线媒体真实性的新问题。由人工智能（AI）生成的视频和图像变得越来越难以与真实媒体区分开来，这促使了新模型的发展以检测人工生成的媒体。虽然已有许多模型试图解决这一问题，但大多数集中于直接处理图像，采用适应卷积神经网络（CNN）或递归神经网络（RNN），这些模型会直接操作视频图像数据。本文介绍了一种仅使用面部标志进行深度伪造检测的方法。通过使用包含真人脸深伪视频和真实视频的数据库，本文描述了提取用于深度伪造检测的面部标志的方法，并专注于识别面部运动中的细微不一致之处，而不是处理原始图像。 实验结果表明，这种特征提取技术在各种神经网络模型中都有效，同一批面部标志经过三种不同神经网络模型测试后，显示出令人鼓舞的表现指标，证明其具有潜在的实际应用价值。本文讨论的发现包括递归神经网络（RNN）和人工神经网络（ANN）模型分别达到了96%至93%的准确率，而卷积神经网络（CNN）模型则维持在78%左右。 这项研究通过提供一种与各种神经网络模型兼容的面部特征提取方法来挑战单纯依靠原始图像处理识别深度伪造视频这一假设，并且这种新方法所需的参数更少。
URL
https://arxiv.org/abs/2507.18815
