Forensic Video Steganalysis in Spatial Domain by Noise Residual Convolutional Neural Network

• URL: http://arxiv.org/abs/2305.18070v1
• Date: Mon, 29 May 2023 13:17:20 GMT
• Title: Forensic Video Steganalysis in Spatial Domain by Noise Residual Convolutional Neural Network
• Authors: Mart Keizer, Zeno Geradts, Meike Kombrink
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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

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