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
@article{arxiv.2305.18070,
title = {Forensic Video Steganalysis in Spatial Domain by Noise Residual Convolutional Neural Network},
author = {Mart Keizer and Zeno Geradts and Meike Kombrink},
journal= {arXiv preprint arXiv:2305.18070},
year = {2023}
}