Forensic Video Steganalysis in Spatial Domain by Noise Residual Convolutional Neural Network
This work addresses forensic steganalysis for video security, but it is incremental as it applies an existing CNN method to a new video dataset.
The paper tackles the problem of detecting hidden messages in videos by using a convolutional neural network (CNN) to analyze noise residuals in the spatial domain, achieving a detection rate of 99.96%.
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