Reinforcement Learning Aided Network Architecture Generation for JPEG Image Steganalysis

Document Type

Conference Proceeding

Publication Date

6-22-2020

Abstract

The architectures of convolutional neural networks used in steganalysis have been designed heuristically. In this paper, an automatic Network Architecture Generation algorithm based on reinforcement learning for JPEG image Steganalysis (JS-NAG) has been proposed. Different from the automatic neural network generation methods in computer vision which are based on the strong content signals, steganalysis is based on the weak embedded signals, thus needs specific design. In the proposed method, the agent is trained to sequentially select some high-performing blocks using Q-learning to generate networks. An early stop strategy and a well-designed performance prediction function have been utilized to reduce the search time. To generate the optimal networks, hundreds of networks have been searched and trained on 3 GPUs for 15 days. To further improve the detection accuracy, we make an ensemble classifier out of the generated convolutional neural networks. The experimental results have shown that the proposed method significantly outperforms the current state-of-the-art CNN based methods.

Identifier

85087925629 (Scopus)

ISBN

[9781450370509]

Publication Title

Ih and Mmsec 2020 Proceedings of the 2020 ACM Workshop on Information Hiding and Multimedia Security

External Full Text Location

https://doi.org/10.1145/3369412.3395060

First Page

23

Last Page

32

Grant

61772571

Fund Ref

National Natural Science Foundation of China

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