Image compressed encryption method based on compressed sensing and Chua's circuit

Abstract

The invention discloses an image compressed encryption method based on compressed sensing and a Chua's circuit. The method comprises the following steps of 1, generating a key related to plaintext according to an original image by using SHA-256; 2, performing iteration on initial values x02 and a02 to generate a chaos sequence t' and t'', and generating measurement matrixes FORMULA and FORMULA through performing iteration on t' and t''; 3, measuring sub-bands LH, HH and HL by different compression ratios; 4, quantizing to acquire an integer sequence z, and forming a matrix z'; 5, acquiring a diffusion matrix f'; 6, acquiring a matrix LL', and performing an XOR operation on the diffusion matrix f'; and 7, combining four sub-bands such as LL, LH, HL and HH, and scrambling the chaos sequencegenerated by x04 and a04 to acquire a final compressed encryption image E. According to the method the data transmission quantity can be reduced, the robustness of the image can also be enhanced, thesecurity is high, and particularly, the method is good in performance when being used for resisting shearing attacks and noise attacks.

Description

technical field [0001] The invention relates to the technical field of image compression and encryption, in particular to an image compression and encryption method based on compressed sensing and Chua's circuit. Background technique [0002] Image compression encryption refers to encrypting images while reducing the data volume of encrypted images and saving transmission space. The image encryption algorithm proposed today uses the chaotic system for encryption, because the chaotic system has ergodicity, unpredictability, mixed properties, initial key and system parameter sensitivity, and is an ideal method to diffuse and disturb the image. pixels. In addition, compressive sensing is also suitable for this field because it can perform both compression and encryption. Among them, many existing algorithms use the entire measurement matrix as the key of the algorithm, which will waste too much transmission space, such as Gaussian random matrix, Bernoulli matrix, partial Hada...

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Problems solved by technology

[0004] This type of measurement matrix has strong randomness, but when it is used for encryption, it needs to transmit more data to the decryption end, and use the reconstruction algorithm to obtain the original image, which will occupy a large transmission space, weaken the compression sensing and reduce the transmission The advantage of the amount of data

[0005] In addition, applying the CS theory directly to the original image may lead to poor robustness during the transmission of the encrypted image. When the encrypted image suffers from continuous packet loss, malicious clipping attack and noise attack, it is difficult for the receiver to recognize the reconstructed image, so Also consider increasing image robustness when using compressed sensing

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