Classification of Multiple Steganographic Algorithms Using Hierarchical CNNs and ResNets
- Authors
- Kang, Sanghoon; Park, Hanhoon; Park, Jong-Il
- Issue Date
- Apr-2021
- Publisher
- Springer Science and Business Media Deutschland GmbH
- Keywords
- Convolutional neural network; Hierarchical structure; Image steganography; Quinary classification; Residual neural network; Steganalysis
- Citation
- Lecture Notes in Networks and Systems, v.149, pp.365 - 373
- Indexed
- SCOPUS
- Journal Title
- Lecture Notes in Networks and Systems
- Volume
- 149
- Start Page
- 365
- End Page
- 373
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/142119
- DOI
- 10.1007/978-981-15-7990-5_36
- ISSN
- 2367-3370
- Abstract
- In general, image deformations caused by different steganographic algorithms are extremely small and of high similarity. Therefore, detecting and identifying multiple steganographic algorithms are not easy. Although recent steganalytic methods using deep learning showed highly improved detection accuracy, they were dedicated to binary classification, i.e., classifying between cover images and their stego images generated by a specific steganographic algorithm. In this paper, we aim at achieving quinary classification, i.e., detecting (=classifying between stego and cover images) and identifying four spatial steganographic algorithms (LSB, PVD, WOW, and S-UNIWARD), and propose to use a hierarchical structure of convolutional neural networks (CNN) and residual neural networks (ResNet). Experimental results show that the proposed method can improve the classification accuracy by 17.71% compared to the method that uses a single CNN.
- Files in This Item
-
Go to Link
- Appears in
Collections - 서울 공과대학 > 서울 컴퓨터소프트웨어학부 > 1. Journal Articles
![qrcode](https://api.qrserver.com/v1/create-qr-code/?size=55x55&data=https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/142119)
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.