Robust image compression‐encryption via scrambled block bernoulli sampling with diffusion noise

Abstract

This paper proposed an image compression‐encryption scheme based on compressive sensing theory, which achieves high security, strong robustness, and high rate‐distortion performance. First, the denoising preprocessing strategy is applied at the encoder side, which can enhance the rate‐distortion performance without sacrificing security and robustness. Second, the preprocessed image is randomly down‐sampled using scrambled block Bernoulli sampling with diffusion noise (SBBS‐DN), which is generated by combining a hyper‐chaotic system and SHA256 hash of the plain image. Third, a deep‐learned plug‐and‐play is embedded prior for plain image reconstruction at the decoder side. Simulation results show that the proposed scheme has desirable security performance (being resistant to different attacks), high R‐D performance (PSNR gains over 1.3 dB than JPEG at 0.50 bpp compression ratio), and high error resilience (reconstructed 29.92 dB at 0.50 bpp compression ratio even with 50% bit loss).