## 1 Introduction
This challenge has been solved through the development of compressive sensing. [PERSON] et al. proposed a method based on compressive sensing (CS) for compression encryption [12]. Compressive sensing compresses and encrypts data simultaneously; this not only reduces network communication bandwidth but also enhances system security. However, while using compressed sensing cryptography, the entire assessment matrix must be saved; moreover, once compressive sensing is merged with a chaotic map, only the matrix's generation variables must be saved. The system's confidentiality could be further enhanced by using the chaos system's sensitivity. In this method, the innovative and universal chaotic structure generates the chaotic map employed in the strategy, which widens the chaotic area of the chaos structure and enhances its effectiveness. CS using the chaos measurement matrices provides a high data sensitivity, according to [PERSON] et al. [13]. Nevertheless, the plaintext sensitivity provided by CS may be significantly diminished due to the quantification performed following CS. Authors in this method developed a novel CS-compression-encryption architecture that makes use of the CS's intrinsic characteristic of providing robust plaintext sensitivity for the proposed technique with minimal additional processing. Meanwhile, a chaos-based substitution box (S-box) building algorithm is being developed. In one more method of CS, [PERSON] et al. [14] proposed a CS and cyclic shift hypothesis-based digital image compression-encryption framework. First, the digital image is compressed using a random Gauss sequence and sparse transform. Moreover, after compressive sensing, cyclic shift and diffusion processes are established. The image encryption method relies on the 2D logistic sine map is also presented by [PERSON] et al. [15]. In the chaotic area, permutation, modulation, and diffusion are all part of the encryption process. The permutation operation is performed cyclically in both the column and row orientations simultaneously. In this work, next, a permuted image modulation function generates the encrypted image, followed by column-wise diffusion. It solves the problem of typical encryption algorithms that need a lot of pixel shuffle before the diffusion phase. In another work presented by [PERSON] et al., color image encryption is done using DNA computing and non-degenerate discrete hyperchaos. Here simulation results and positive Lyapunov exponent indicates, a fast, more secure, and robust image encryption scheme [16]. To accomplish image encryption and compression with reconstructing reliability and strong security, [PERSON] et al. [7] presented a visual compression and encryption system built on CS and the Fourier transform. Encryption and compression are integrated by utilizing the CS characteristic. To circumvent the security restrictions of releasing the energy content of the plaintext from ciphertext and reprocessing the assessment matrices, a chaos procedure and 2D-FRFT are used to perform encryption. Additionally, diffusion encryption based on double random phase cryptography in 2D may avoid reconstruction resilience loss. In a study proposed by [PERSON] et al. [17], singular value decomposition (SVD) in the discrete cosine Stockwell transform (DCST) is used to encrypt color images. SVD's DCST properties and permutation method give more secure data. Decrypting encrypted images requires knowing all the keys and their contents. The work's robustness study shows that if one parameter is wrong and the others are right, it is practically difficult to estimate the original image information. [PERSON] et al. [18] offer another encryption compression approach based on SVD. Here, the sender used DWT to reconstruct the image. A pseudo-random integer sequence and a pseudo-random permutation are used to encrypt the approximation and detail sub-bands. The channel provider 'losslessly' compresses the encrypted approximation sub-band, reducing image size without compromising quality.