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Image steganalysis based on attention augmented convolution

  • 1205: Emerging Technologies for Information Hiding and Forensics in Multimedia Systems
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Abstract

Steganalysis, as the opposite technique to steganography, has been applied to determine whether secret information is embedded in an image. The existing adaptive steganography methods embed secret information in different regions of an image with different probabilities. However, it is difficult for most steganalysis models to make a targeted attention of steganography regions of images, which reduces the detection accuracy of the steganalysis models when detecting the adaptive steganography methods. Aiming to detect the JPEG-based adaptive steganography, this paper proposes a model called Steganalysis Attention Augmented Network (SAANet). The proposed model uses attention augmented convolution instead of traditional convolution, so that the network assigns more learning weights to the steganographic area, and guides the network to better learn features that are beneficial to steganalysis. Thereby improving the learning ability and training effect of the model. As far as we know, we are the first to directly apply attention to the backbone structure of the network model to assist in image steganalysis. And our work improves the accuracy and operating efficiency of the steganalysis model. Experiments show that compared with the current steganalysis models, the model proposed in this paper obtains a competitive detection performance. The current adaptive steganography algorithm achieves a detection accuracy of up to 96.68%, while 95.22% under mismatch conditions, and the testing time is 28 s, indicating that the proposed model has a certain generalization performance and practicability.

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Authors and Affiliations

  1. College of Cryptographic Engineering, Engineering University of PAP, Xi’an, 710086, China

    Siyuan Huang, Minqing Zhang, Xinliang Bi & Yongjun Kong

  2. Key Laboratory for Network and Information Security of PAP, Engineering University of PAP, Xi’an, 710086, China

    Siyuan Huang, Minqing Zhang, Yan Ke, Xinliang Bi & Yongjun Kong

  3. Counterterrorism Command & Information Engineering Joint Lab, Urumqi Campus of Engineering University of PAP, Urumqi, 830049, China

    Yan Ke

Authors
  1. Siyuan Huang
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  2. Minqing Zhang
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  3. Yan Ke
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  4. Xinliang Bi
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  5. Yongjun Kong
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Correspondence to Siyuan Huang.

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Huang, S., Zhang, M., Ke, Y. et al. Image steganalysis based on attention augmented convolution. Multimed Tools Appl 81, 19471–19490 (2022). https://doi.org/10.1007/s11042-021-11862-4

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  • DOI: https://doi.org/10.1007/s11042-021-11862-4

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