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 net-work(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 stegano-grapher detection.Experiments show that the designed model can successfully attack a variety of batch stegano-graphy and the corresponding strategies.
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
Key words
Image steganograher detection/graph convolutional network(GCN)/multiple-instance learning(MIL)/bag of instances representation
维普
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