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Halftone Image Steganography Based on Minimizing Distortion with Pixel Density Transition

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Artificial Intelligence and Security (ICAIS 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12737))

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Abstract

Many advanced halftone steganographic schemes focus only on the distortion of human visual perception or the distortion according to statistics. In this paper, a halftone image steganography based on minimizing distortion with density transition is proposed which aims at utilizing the entropy model and pixel density to resist potential steganalysis. First, the entropy model is established on the image database to describe the texture content and transformed to the preliminary distortion score map. Because the form of texture presentation is distinct between halftone images and ordinary binary images, the feature of pixel density is introduced to represent the local intensity in images. Then the pixel density transition adjustment based on the entropy model is presented, which makes the distortion score more reliable. The final additive distortion map is generated by combining the entropy model and the strategy of density transition. To play the advantage of distortion measurement, syndrome-trellis code (STC) is applied with the distortion map to minimize the embedding distortions. Experimental results demonstrate that compared with other halftone steganographic schemes, the proposed method achieves high statistical security and great visual quality with considerable embedding capacity.

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Acknowledgements

This work is supported by the Key Areas R&D Program of Guangdong (No. 2019B010136002), the National Natural Science Foundation of China (No. U2001202, No. 62072480, No. U1736118), the National Key R&D Program of China (No. 2019QY2202, No. 2019QY(Y)0207), the Key Scientific Research Program of Guangzhou (No. 201804020068).

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Authors and Affiliations

  1. School of Computer Science and Engineering, Guangdong Key Laboratory of Information Security Technology, Ministry of Education Key Laboratory of Machine Intelligence and Advanced Computing, Sun Yat-sen University, Guangzhou, 510006, China

    Mujian Yu, Junwei Luo, Bozhi Xu & Wei Lu

  2. Guangdong Science and Technology Innovation, Monitoring and Research Center, Guangzhou, 510033, China

    Guoliang Chen

Authors
  1. Mujian Yu
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  2. Junwei Luo
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  3. Bozhi Xu
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  4. Guoliang Chen
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  5. Wei Lu
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Corresponding author

Correspondence to Wei Lu .

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Editors and Affiliations

  1. Nanjing University of Information Science and Technology, Nanjing, China

    Xingming Sun

  2. Nanjing University of Information Science and Technology, Nanjing, China

    Xiaorui Zhang

  3. Jinan University, Guangzhou, China

    Zhihua Xia

  4. Purdue University, West Lafayette, IN, USA

    Elisa Bertino

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Yu, M., Luo, J., Xu, B., Chen, G., Lu, W. (2021). Halftone Image Steganography Based on Minimizing Distortion with Pixel Density Transition. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2021. Lecture Notes in Computer Science(), vol 12737. Springer, Cham. https://doi.org/10.1007/978-3-030-78612-0_34

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  • DOI: https://doi.org/10.1007/978-3-030-78612-0_34

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-78611-3

  • Online ISBN: 978-3-030-78612-0

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