Research Article
A CNN-Based HEVC Video Steganalysis Against DCT/DST-Based Steganography
@INPROCEEDINGS{10.1007/978-3-031-06365-7_16, author={Zhenzhen Zhang and Henan Shi and Xinghao Jiang and Zhaohong Li and Jindou Liu}, title={A CNN-Based HEVC Video Steganalysis Against DCT/DST-Based Steganography}, proceedings={Digital Forensics and Cyber Crime. 12th EAI International Conference, ICDF2C 2021, Virtual Event, Singapore, December 6-9, 2021, Proceedings}, proceedings_a={ICDF2C}, year={2022}, month={6}, keywords={Video steganalysis Steganography DCT/DST HEVC CNN}, doi={10.1007/978-3-031-06365-7_16} }- Zhenzhen Zhang
Henan Shi
Xinghao Jiang
Zhaohong Li
Jindou Liu
Year: 2022
A CNN-Based HEVC Video Steganalysis Against DCT/DST-Based Steganography
ICDF2C
Springer
DOI: 10.1007/978-3-031-06365-7_16
Abstract
The development of video steganography has sparked ever-increasing concerns over video steganalysis. In this paper, a novel steganalysis approach against Discrete Cosine/Sine Transform (DCT/DST) based steganography for High Efficiency Video Coding (HEVC) video is proposed. The distortion of DCT/DST-based HEVC steganography and the impact on pixel value of HEVC videos is firstly analyzed. Based on the analysis, a convolutional neural network (CNN) is designed. The proposed CNN is mainly composed of three parts, i.e. residual convolution layer, feature extraction and binary classification. In the feature extraction part, a steganalysis residual block module and a squeeze-and-excitation (SE) block are designed to improve the network’s representation ability. In comparison to the existing steganalysis methods, experimental results show that the proposed network performs better to detect DCT/DST-based HEVC steganography.
