This research evaluates a convolutional neural network (CNN) based approach to forensic video steganalysis. A video steganography dataset is created to train a CNN to conduct forensic steganalysis in the spatial domain. We use a noise residual convolutional neural network to detect embedded secrets since a steganographic embedding process will always result in the modification of pixel values in video frames. Experimental results show that the CNN-based approach can be an effective method for forensic video steganalysis and can reach a detection rate of 99.96%. Keywords: Forensic, Steganalysis, Deep Steganography, MSU StegoVideo, Convolutional Neural Networks

翻译：本研究评估了一种基于卷积神经网络（CNN）的取证视频隐写分析方法。通过构建视频隐写数据集，训练CNN在空域中进行取证隐写分析。由于隐写嵌入过程始终会改变视频帧中的像素值，我们采用噪声残差卷积神经网络来检测嵌入的秘密信息。实验结果表明，基于CNN的方法可成为取证视频隐写分析的有效手段，其检测率可达99.96%。关键词：取证，隐写分析，深度隐写，MSU StegoVideo，卷积神经网络