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<p>With the rapid development of digital image transmission, ensuring the security and efficiency of image data transmission and storage has become a hot topic. In order to reduce the image reconstruction time without reducing the quality of the image reconstruction, this paper introduces a robust encryption and compression scheme for sequence images, leveraging semi-tensor product compressive sensing and two-dimensional entire parameters hyperchaotic map. By employing an asymmetric encryption approach on key, the hash value of the plaintext image is securely transmitted. The semi-tensor product compressive sensing model significantly reduces the sampling complexity and dimension of the measurement matrix, while multiple dynamic S-boxes and diffusion operations ensure high security. Experimental results demonstrate that the proposed chaotic map outperforms existing two-dimensional chaotic maps in terms of trajectory, Lyapunov exponent, permutation entropy, sample entropy, and 0–1 test. The encryption scheme exhibits excellent performance in resisting brute-force, known/chosen-plaintext, noise, and statistical attacks. The reconstructed images maintain high quality, with PSNR values exceeding 30&#xa0;dB at a compression ratio of 0.5. Overall, this study presents a novel and effective approach for secure and efficient image transmission and the source code of this paper is openly available from <a href="https://github.com/YangJieTYUT/SIETC">https://github.com/YangJieTYUT/SIETC</a>.</p>

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Robust encryption and compression of sequential images using STP-CS and 2D-EPHM: a novel hybrid symmetric-asymmetric approach

  • 期刊论文

  • 发表日期：

    2025年3月23日

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  • Feng Xiufang,
  • Yang Jie,
  • Wu Yongfei,
  • Zhang Hao

摘要

With the rapid development of digital image transmission, ensuring the security and efficiency of image data transmission and storage has become a hot topic. In order to reduce the image reconstruction time without reducing the quality of the image reconstruction, this paper introduces a robust encryption and compression scheme for sequence images, leveraging semi-tensor product compressive sensing and two-dimensional entire parameters hyperchaotic map. By employing an asymmetric encryption approach on key, the hash value of the plaintext image is securely transmitted. The semi-tensor product compressive sensing model significantly reduces the sampling complexity and dimension of the measurement matrix, while multiple dynamic S-boxes and diffusion operations ensure high security. Experimental results demonstrate that the proposed chaotic map outperforms existing two-dimensional chaotic maps in terms of trajectory, Lyapunov exponent, permutation entropy, sample entropy, and 0–1 test. The encryption scheme exhibits excellent performance in resisting brute-force, known/chosen-plaintext, noise, and statistical attacks. The reconstructed images maintain high quality, with PSNR values exceeding 30 dB at a compression ratio of 0.5. Overall, this study presents a novel and effective approach for secure and efficient image transmission and the source code of this paper is openly available from https://github.com/YangJieTYUT/SIETC.