In accordance with regulations and requirements, the editorial department's website domain has been changed to arocmag.cn. The original domain (arocmag.com) will be discontinued after Dec. 31st, 2024.
See More
App. Res. of Comp. Accepted Paper
Vol. 41, 2024 No. 12

Active defense method for information hiding based on self-supervised learning pbs-net and channel purification

Ma Yuanyuana,b
Zhao Ying’aoa
Xu Fuyonga
Zhang Qianqiana,b
Xin Xianweia,b
a. College of Computer & Information Engineering, b. Engineering Lab of Intelligence Business & Internet of Things, Henan Normal University, Henan Xinxiang 453007, China
DOI: 10.19734/j.issn.1001-3695.2024.01.0108

Abstract

As the opposite of information hiding, active defense technology can block the transmission of illegal hidden communication. However, the existing active defense methods rely too much on cover-stego image pair and cannot actively defend the unknown stego images, which reduce the bit error ratio in the actual social network. To solve these problems, this paper proposes an active defense method of self-supervised learning blind-spot network and channel purification in order to completely block the transmission of secret information without being noticed by both communication parties. Firstly, the method used pixel shuffling sampling to reduce the spatial correlation of pixels in stego images, and improve the learning mode from supervised learning to self-supervised learning. Secondly, it integrated centrally masked convolutions and dilated convolution residual blocks to eliminate secret information. Finally, it obtained the channel purification module to improve the image texture details. The method does not need any prior knowledge of information hiding schemes and manual operation, so that it can eliminate secret information before hosts receive suspicious images. The experimental results show that this method has high secret information destruction effect and high image quality, and can achieve 100% defense success rate and block covert communication in social networks. At the same time, under different payload data sets, the proposed method is compared with SC-Net method and AO-Net method, and the secret information elimination is improved by 14.14% and 2.91% respectively. The image quality was improved by 9.14% and 43.34% respectively.

Foundation Support

国家自然科学基金资助项目(62002103)
河南省优秀青年科学基金资助项目(222300420058)
河南省高等学校重点科研项目资助项目(24A520019)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.01.0108
Publish at: Application Research of Computers Accepted Paper, Vol. 41, 2024 No. 12

Publish History

[2024-09-02] Accepted Paper

Cite This Article

马媛媛, 赵颖澳, 徐富永, 等. 基于自监督学习PBS-Net和通道提纯的信息隐藏主动防御方法 [J]. 计算机应用研究, 2024, 41 (12). (2024-09-11). https://doi.org/10.19734/j.issn.1001-3695.2024.01.0108. (Ma Yuanyuan, Zhao Ying’ao, Xu Fuyong, et al. Active defense method for information hiding based on self-supervised learning pbs-net and channel purification [J]. Application Research of Computers, 2024, 41 (12). (2024-09-11). https://doi.org/10.19734/j.issn.1001-3695.2024.01.0108. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

Indexed & Evaluation

  • The Second National Periodical Award 100 Key Journals
  • Double Effect Journal of China Journal Formation
  • the Core Journal of China (Peking University 2023 Edition)
  • the Core Journal for Science
  • Chinese Science Citation Database (CSCD) Source Journals
  • RCCSE Chinese Core Academic Journals
  • Journal of China Computer Federation
  • 2020-2022 The World Journal Clout Index (WJCI) Report of Scientific and Technological Periodicals
  • Full-text Source Journal of China Science and Technology Periodicals Database
  • Source Journal of China Academic Journals Comprehensive Evaluation Database
  • Source Journals of China Academic Journals (CD-ROM Version), China Journal Network
  • 2017-2019 China Outstanding Academic Journals with International Influence (Natural Science and Engineering Technology)
  • Source Journal of Top Academic Papers (F5000) Program of China's Excellent Science and Technology Journals
  • Source Journal of China Engineering Technology Electronic Information Network and Electronic Technology Literature Database
  • Source Journal of British Science Digest (INSPEC)
  • Japan Science and Technology Agency (JST) Source Journal
  • Russian Journal of Abstracts (AJ, VINITI) Source Journals
  • Full-text Journal of EBSCO, USA
  • Cambridge Scientific Abstracts (Natural Sciences) (CSA(NS)) core journals
  • Poland Copernicus Index (IC)
  • Ulrichsweb (USA)