Towards automatic embedding cost learning for JPEG steganography

Document Type

Conference Proceeding

Publication Date

7-2-2019

Abstract

Current mainstream methods for digital image steganography are content adaptive. That is, the secret messages are embedded in the complicated region in the cover image while minimizing the embedding distortion so as to suppress statistical detectability. Since there is already a practical encoding scheme for data embedding near the payload-distortion bound, the design of the embedding cost function becomes a deterministic part in steganography. Unlike the traditional heuristic hand-crafted method, this paper proposes a novel generative adversarial network based framework to automatically learn the embedding cost function for JPEG steganography. The proposed framework consists of a generator, a gradient-descent friendly inverse discrete cosine transformation module, an embedding simulator and a discriminator for steganalysis. Through training the generator and discriminator in alternation, the embedding cost function can finally be obtained by the trained generator. Experimental results demonstrate that our method can automatically learn a reasonable embedding cost function and achieve a satisfying performance.

Identifier

85069957865 (Scopus)

ISBN

[9781450368216]

Publication Title

Ih and Mmsec 2019 Proceedings of the ACM Workshop on Information Hiding and Multimedia Security

External Full Text Location

https://doi.org/10.1145/3335203.3335713

First Page

37

Last Page

46

Grant

61772571

Fund Ref

National Natural Science Foundation of China

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