ARES: On Adversarial Robustness Enhancement for Image Steganographic Cost Learning-Reference-Cited by-同舟云学术

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ARES: On Adversarial Robustness Enhancement for Image Steganographic Cost Learning

Published:2024 Issue: Volume:26 Page:6542-6553
ISSN:1520-9210
Container-title:IEEE Transactions on Multimedia
language:
Short-container-title:IEEE Trans. Multimedia

Author:

Cui Qi¹^ORCID,Zhou Zhili²^ORCID,Meng Ruohan¹^ORCID,Wang Shaowei²^ORCID,Yan Hongyang²^ORCID,Wu Q. M. Jonathan³^ORCID

Affiliation:

1. Engineering Research Center of Digital Forensics, Ministry of Education, the School of Computer Science, Nanjing University of Information Science and Technology, Nanjing, China

2. Institute of Artificial Intelligence, Guangzhou University, Guangzhou, China

3. Department of Electrical and Computer Engineering, University of Windsor, Windsor, Canada

Funder

National Natural Science Foundation of China

Guangdong Natural Science Funds for Distinguished Young Scholar

China Scholarship Council

Guangzhou Research Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6046/10384483/10398515.pdf?arnumber=10398515

Reference62 articles.

1. Research on covert communication channel based on modulation of common compressed speech codec

2. Practical methods for minimizing embedding impact in steganography

3. Using High-Dimensional Image Models to Perform Highly Undetectable Steganography

4. Minimizing Additive Distortion in Steganography Using Syndrome-Trellis Codes

5. Designing Near-Optimal Steganographic Codes in Practice Based on Polar Codes

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