摘要
隐写者检测通过设计模型检测在批量图像中嵌入秘密信息进行隐蔽通信的隐写者,对解决非法使用隐写术的问题具有重要意义.本文提出一种基于多示例学习图卷积网络(Multiple-instance learning graph convolutional network,MILGCN)的隐写者检测算法,将隐写者检测形式化为多示例学习(Multiple-instance learning, MIL)任务.本文中设计的共性增强图卷积网络(Graph convolutional network, GCN)和注意力图读出模块能够自适应地突出示例包中正示例的模式特征,构建有区分度的示例包表征并进行隐写者检测.实验表明,本文设计的模型能够对抗多种批量隐写术和与之对应的策略.
Steganographer detection aims to solve the problem of illegal use of batch steganography by designing models to detect steganographers who embed secret information in images for covert communication.This paper proposes a novel steganographer detection algorithm called as multiple-instance learning graph convolutional network(MILGCN)to formalize steganography detection as a multiple-instance learning(MIL)task.The commonness enhancement graph convolutional network(GCN)and attention graph readout module designed in this paper can adaptively highlight the positive instance pattern and construct distinguishable bag representations for steganographer detection.Experiments show that the designed model can successfully attack a variety of batch steganography and the corresponding strategies.
作者
钟圣华
张智
ZHONG Sheng-Hua;ZHANG Zhi(College of Computer Science and Software Engineering,Shenzhen University,Shenzhen 518060;Department of Computing,The Hong Kong Polytechnic University,Hong Kong 999077)
出处
《自动化学报》
EI
CAS
CSCD
北大核心
2024年第4期771-789,共19页
Acta Automatica Sinica
基金
广东省自然科学基金(2023A1515012685,2023A1515011296)
国家自然科学基金(62002230,62032015)资助。
关键词
图像隐写者检测
图卷积网络
多示例学习
示例包表征
Image steganograher detection
graph convolutional network(GCN)
multiple-instance learning(MIL)
bag of instances representation
