On the security and robustness of encryption via compressed sensing

Abstract

The compressed sensing (CS)

Optimal Permutation Based Block Compressed Sensing for Image Compression Applications

“…Secondly, CS-based image coding is very promising method in encrypted image compression applications. It has been suggested in [6] that CS framework leads to an encryption scheme, where the sensing matrix can be considered as an encryption key. Therefore, the CS-based image compression methods like proposed in this paper is useful in contexts where robust coding [5] and encrypted compression [6] are very important, but the subsequent storage and communication of quantized samples is less constricted.…”

“…, 4 is the i-th block of X. Apparently, the block sparsity vector of X is K = [16,6,6, 0] and the average block sparsity level of X isK = 7. Since the nonzero entries of X aren't evenly distributed among the blocks, the maximum block sparsity level K max = 16 is far greater than the average sparsity levelK = 7.…”

“…Obviously, the block sparsity level vector of X † is K = [6,8,8,6]. The nonzero entries of X † are almost evenly distributed among the blocks.…”

“…However, it has been shown that traditional CS combined with ordinary quantization is not a good compression technique in view of compression efficiency [4]. Nonetheless, the CS-based image compression has become a hot topic in recent years, because it has been demonstrated very efficiency in some special image compression applications, such as robust image coding [5] and encrypted image compression [6].…”

A 3-D Video Watermark Embedding Technology in DCT-CS Domain

“…Furthermore, ref. [15] shows that CS transformation can achieve computationally secure encryption. In [16], Zhang et al proposed a novel watermarking scheme which employed a CS technique to retrieve the coefficients by exploiting the sparseness in the DCT domain.…”