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

arXiv: https://arxiv.org/abs/2305.18070 · HTML: https://mlsanity.com/paper/2305.18070/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 17 references that are in the map.
- First posted on arXiv 29 May 2023.

## Builds on (most cited first, 2 of 2)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [From Lifestyle Vlogs to Everyday Interactions](https://mlsanity.com/paper/1712.02310/) (2017)

## Abstract (arXiv)

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