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<p>In this paper, an Adaptive Hybrid Compressive Sensing framework with Hierarchical Optimized Multi-Domain Security (AHC–HOMS) is proposed for secure and efficient image transmission. The proposed approach jointly integrates signal sparsification, adaptive compression, and multi-layer encryption within a unified pipeline. A hybrid DWT–DCT sparsifying transform is employed to enhance signal sparsity across spatial and frequency domains, while an adaptive block-wise compressive sensing strategy dynamically allocates measurements based on local texture complexity to balance compression efficiency and reconstruction fidelity. To ensure security, a hierarchical dual-layer encryption mechanism is introduced, where the most significant transform coefficients are protected using AES and the compressed measurements are selectively scrambled using chaos-seeded random mapping. Extensive experiments conducted on multiple benchmark images with varying structural and textural characteristics demonstrate the robustness and effectiveness of the proposed framework. For legitimate users, AHC–HOMS achieves PSNR values in the range of 24.08–29.43 dB, SSIM values up to 0.84, and consistently low reconstruction error, indicating reliable perceptual and structural recovery. In contrast, unauthorized reconstructions exhibit severe degradation, with PSNR values close to 10 dB and SSIM well below 0.2, rendering the recovered content visually meaningless. Security analysis further confirms strong resistance against differential attacks, with NPCR values exceeding 98% and UACI values in the range of 21–24% across all test images. Overall, the proposed AHC–HOMS framework provides a balanced trade-off between compression performance, reconstruction quality, and security, outperforming conventional CS-only and encryption-only schemes, and is well suited for secure image transmission in resource-constrained and wireless sensing applications.</p>

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Adaptive Hybrid Compressive Sensing with Hierarchical Multi-Domain Encryption for Secure Image Transmission

  • 期刊论文

  • 发表日期：

    2026年5月4日

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  • Narendra Kumar Swami,
  • Hemant Kumar Gupta

摘要

In this paper, an Adaptive Hybrid Compressive Sensing framework with Hierarchical Optimized Multi-Domain Security (AHC–HOMS) is proposed for secure and efficient image transmission. The proposed approach jointly integrates signal sparsification, adaptive compression, and multi-layer encryption within a unified pipeline. A hybrid DWT–DCT sparsifying transform is employed to enhance signal sparsity across spatial and frequency domains, while an adaptive block-wise compressive sensing strategy dynamically allocates measurements based on local texture complexity to balance compression efficiency and reconstruction fidelity. To ensure security, a hierarchical dual-layer encryption mechanism is introduced, where the most significant transform coefficients are protected using AES and the compressed measurements are selectively scrambled using chaos-seeded random mapping. Extensive experiments conducted on multiple benchmark images with varying structural and textural characteristics demonstrate the robustness and effectiveness of the proposed framework. For legitimate users, AHC–HOMS achieves PSNR values in the range of 24.08–29.43 dB, SSIM values up to 0.84, and consistently low reconstruction error, indicating reliable perceptual and structural recovery. In contrast, unauthorized reconstructions exhibit severe degradation, with PSNR values close to 10 dB and SSIM well below 0.2, rendering the recovered content visually meaningless. Security analysis further confirms strong resistance against differential attacks, with NPCR values exceeding 98% and UACI values in the range of 21–24% across all test images. Overall, the proposed AHC–HOMS framework provides a balanced trade-off between compression performance, reconstruction quality, and security, outperforming conventional CS-only and encryption-only schemes, and is well suited for secure image transmission in resource-constrained and wireless sensing applications.