CN107835333A - More image encryption methods based on compressed sensing relevance imaging - Google Patents
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- CN107835333A CN107835333A CN201711169514.XA CN201711169514A CN107835333A CN 107835333 A CN107835333 A CN 107835333A CN 201711169514 A CN201711169514 A CN 201711169514A CN 107835333 A CN107835333 A CN 107835333A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N1/00—Scanning, transmission or reproduction of documents or the like, e.g. facsimile transmission; Details thereof
- H04N1/32—Circuits or arrangements for control or supervision between transmitter and receiver or between image input and image output device, e.g. between a still-image camera and its memory or between a still-image camera and a printer device
- H04N1/32101—Display, printing, storage or transmission of additional information, e.g. ID code, date and time or title
- H04N1/32144—Display, printing, storage or transmission of additional information, e.g. ID code, date and time or title embedded in the image data, i.e. enclosed or integrated in the image, e.g. watermark, super-imposed logo or stamp
- H04N1/32149—Methods relating to embedding, encoding, decoding, detection or retrieval operations
- H04N1/32267—Methods relating to embedding, encoding, decoding, detection or retrieval operations combined with processing of the image
- H04N1/32272—Encryption or ciphering
Abstract
The present invention relates to a kind of more image encryption methods based on compressed sensing relevance imaging, more images of collection are encrypted first：Different pictures is subjected to Unified coding and then deviation is gradually overlapped at an appropriate location by the image after coding, finally imaging is associated with the image after Stochastic Modulation signal and superposition, obtain a series of information after encryptions, that is ciphertext, coding information and Stochastic Modulation signal are combined as key；During information transfer, pass loser and recipient both sides share the key, the ciphertext is transmitted between both sides；Key and ciphertext are compressed into perception when finally decrypting to calculate, reconstruct cleartext information, i.e. multiple image.This encryption method realizes coding and relevance imaging double layer encryption, adds the difficulty of decoding.When decryption, realize that double layer encryption once unties mode, while compressed sensing algorithm can reconstruct cleartext information under less sampling number, greatly improve decryption efficiency, and new method is provided for more image encryptions.
Description
Technical field
The present invention relates to a kind of image encryption technology, more particularly to a kind of more images based on compressed sensing relevance imaging add
Decryption method.
Background technology
In order to ensure the safe transmission of digital picture, large quantities of digital image encryption methods have been emerged in large numbers, wherein with
The Double random phase technology of the propositions such as Refregier is most widely applied.But with the development of technology, single adds
Secret skill art can not meet to be actually needed, and for more image encryptions, existing technology uses Gerchberg-Saxton phases mostly
Position iterative algorithm, but in order to mitigate system complexity, this method tackles DeGrain in the defence of plaintext attack again.
Relevance imaging realizes the separation of imaging detection and object, it also avoid being disturbed because carrying the light field of object information
The problem of random and unavailable clear image.Information is mainly carried on object to be imaged by relevance imaging encipherment scheme,
So that diffusion of information is in the space distribution information of object, and distributes key in a manner of one-time pad, the randomness of key increases
The strong randomness of ciphertext, and then add the difficulty of decoding.Relevance imaging is applied to image encryption by someone at present, such as profit
With ghost imaging is calculated, Pere etc. realizes single image optical encryption；Chen etc. proposes produces three using Phase Retrieve Algorithm
Key is tieed up, so as to carry out the method for image encryption.But the image encryption technology for being currently based on ghost imaging is to be directed to single width mostly
The encryption of image, and the research for more image encryption technologies is then quite deficient.
The content of the invention
The present invention be directed to the problem of more image encryption technology scarcities, it is proposed that a kind of based on compressed sensing relevance imaging
More image encryption methods, realize the encryption of more images.
The technical scheme is that：A kind of more image encryption methods based on compressed sensing relevance imaging, are specifically included
Following steps：
1) encrypt：First by the different picture T of N widthi(x, y) (i=1,2 ... N) carry out Unified coding, coding information A
(x, y), N width coded images are obtained, are then gradually overlapped the image after coding with the position deviation set, form a width
Aliased image M (x, y)；
With Stochastic Modulation signal Ir(x, y) and aliased image M (x, y) are associated imaging, obtain ciphertext Br；Coding information A
(x, y) and Stochastic Modulation signal Ir(x, y) is combined as key Kr；
2) transmit：During information transfer, pass loser and recipient both sides share the key Kr, transmit between both sides described close
Literary Br；
3) decrypt：Key and ciphertext are compressed into perception during decryption to calculate, reconstruct cleartext information, i.e. N width image moment
Battle array.
N width coded images are obtained in the step 1), if N ＞ 10 by coded image with the position deviation of a pixel gradually
It is overlapped；Coded image is gradually overlapped with the position deviation of two pixels if N≤10, forms a width aliased image M
(x, y).
In the step 1)
Br=∫ dxdyIr(x, y) M (x, y) (r=1,2 ... Z)
Kr(x, y)=A (x, y) × Ir(x, y) (r=1,2 ... Z)
Wherein Z is pendulous frequency, and the size of Z how much decision ciphertexts.
Decrypting process is as follows in the step 3)：
Tcs=T；min||Ti(x, y) | | L1subject to：
Br=∫ dxdyIr(x, y) M (x, y) (r=1, Z)
In formula, Ti(x, y) represents plaintext to be decrypted, i.e. N width image information, and Z represents pendulous frequency, by Ti(x, y) is formed
Matrix as sparse matrix to be asked；The combination K of the Z Stochastic Modulation signals being independently distributed and coding informationr(x, y) is formed
New matrix, as calculation matrix；Z measured value BrThe matrix of composition, as measured value；Calculate TiThe matrix of (x, y) composition is most
Small L1 norms represent, so that it may obtain the cleartext information T of reconstructionCS。
The beneficial effects of the present invention are：More image encryption methods of the invention based on compressed sensing relevance imaging, are used
The mode of double layer encryption completes the encryption to more images, and first layer is Unified coding, realizes the displacement to more images, and will
More image aliasings together, make multiple image be transformed into one every image, substantially increase the difficulty of decoding.The second layer is encoded to pass
It is unified into as algorithm for encryption, the randomness of modulated signal enhances the randomness of ciphertext, and then improves security.Because key packet
Containing coding information and modulated signal, even if code breaker intercepts part of key and ciphertext, ciphertext and the correspondence of key can not be known
Relation, it is impossible to rebuild in plain text.The present invention use double layer encryption, but rebuild plaintext need only to once calculate can be obtained by it is bright
Text, substantially increase decryption efficiency.The present invention being capable of adopting in sampling thheorem requirement of the sampling number far below Naquist simultaneously
Cleartext information is reconstructed well under sample number, is realized under identical design conditions, can transmit more encryption information,
Improve the information content of transmission.
Brief description of the drawings
Fig. 1 is more image encryption method schematic diagrames of the invention based on compressed sensing relevance imaging.
Embodiment
A kind of more image relevance imaging methods based on compressed sensing relevance imaging, key are coding information and modulated signal
Combination, be in plain text more image informations to be encrypted, ciphertext be carry out coding and relevance imaging obtain a series of encryptions after
Information.Method as shown in Figure 1 comprises the following steps：
Step 1 is encrypted：First by the different picture T of N widthi(x, y) (i=1,2 ... N) carry out Unified coding, coding information
For A (x, y), N width coded images are obtained, are then gradually overlapped the image after coding with the position deviation set.If obtain
N width coded images are taken, are gradually overlapped coded image with the position deviation of a pixel if N ＞ 10；It will be compiled if N≤10
Code image is gradually overlapped with the position deviation of two pixels, forms a width aliased image M (x, y).For example, add as shown in Figure 1
Close amount of images is 4, and every width pixel size is 128 × 128, you can it is three-dimensional to be expressed as the image that size is 128 × 128 × 4
Data.Every width figure is encoded, 128 × 128 × 4 coded image will be obtained after 128 × 128 × 4 Image Coding.Because
Amount of images itself is less than 10, so coded image is gradually overlapped with the position deviation of two pixels, i.e., by size be 128 ×
128 × 4 image stereo data is superimposed as the view data of 128 × 138 pixel sizes.
With Stochastic Modulation signal Ir(x, y) and aliased image M (x, y) are associated imaging, will both be multiplied, obtain
Take a series of information after encryptions, ciphertext BrObtained by formula (1).Coding information A (x, y) and Stochastic Modulation signal Ir(x, y) is combined
As key, key KrObtained by formula (2).
Br=∫ dxdyIr(x, y) M (x, y) (r=1,2 ... Z) (1)
Kr(x, y)=A (x, y) × Ir(x, y) (r=1,2 ... Z) (2)
Wherein Z is pendulous frequency, and the size of Z how much decision ciphertexts.
Step (2) is transmitted：During information transfer, pass loser and recipient both sides share the key Kr, transmit between both sides
The ciphertext Br。
Step (3) is decrypted：Key and ciphertext are compressed into perception during decryption to calculate, reconstruct cleartext information, i.e. N width figure
As matrix.
For example, N=4, decrypting process is exactly that ciphertext is reverted into aliased image first, then by aliased image inverting into plain text
(i.e. an image stereo data of multiple images composition).But coding information is all that image is multiplied in itself with Stochastic Modulation signal
Process, therefore in decryption, coding information and Stochastic Modulation signal are combined as key, then reduces and reverts to aliasing figure
The process of picture, directly ciphertext is reconstructed into plain text using compressed sensing.I.e. by the ciphertext that length is Z, 128 × 128 × 4 are reconstructed into
Cleartext information, as 4 width images matrix, per it is one-dimensional is piece image, totally 4 width image.
Decrypting process is as follows：Key and ciphertext are compressed into perception to calculate, rebuild cleartext information, is obtained by formula (3)
Tcs=T；min||Ti(x, y) | | L1subject to： (3)
Br=∫ dxdyIr(x, y) M (x, y) (r=1, Z)
In formula, Ti(x, y) represents plaintext to be decrypted, i.e., more image informations, and Z represents pendulous frequency.By Ti(x, y) is formed
Matrix as sparse matrix to be asked；The combination K of the Z Stochastic Modulation signals being independently distributed and coding informationr(x, y) is formed
New matrix, as calculation matrix；Z measured value BrThe matrix of composition, as measured value.Calculate TiThe matrix of (x, y) composition is most
Small L1 norms represent, it is possible to obtain the cleartext information T of reconstructionCS。
Claims (4)
1. a kind of more image encryption methods based on compressed sensing relevance imaging, it is characterised in that specifically comprise the following steps：
1) encrypt：First by the different picture T of N widthi(x, y) (i=1,2...N), i.e., in plain text, carry out Unified coding, coding information
For A (x, y), N width coded images are obtained, are then gradually overlapped the image after coding with the position deviation set, formed
One width aliased image M (x, y)；
With Stochastic Modulation signal Ir(x, y) and aliased image M (x, y) are associated imaging, obtain ciphertext Br；Coding information A (x,
And Stochastic Modulation signal I y)r(x, y) is combined as key Kr；
2) transmit：During information transfer, pass loser and recipient both sides share the key Kr, the ciphertext B is transmitted between both sidesr；
3) decrypt：Key and ciphertext are compressed into perception during decryption to calculate, reconstruct cleartext information, i.e. N width image array.
2. more image encryption methods based on compressed sensing relevance imaging according to claim 1, it is characterised in that the step
Coded image, is gradually overlapped by rapid 1) middle acquisition N width coded images if N ＞ 10 with the position deviation of a pixel；If N≤
10 are gradually overlapped coded image with the position deviation of two pixels, form a width aliased image M (x, y).
3. more image encryption methods according to claim 1 or claim 2 based on compressed sensing relevance imaging, it is characterised in that institute
State in step 1)
Br=∫ dxdyIr(x, y) M (x, y) (r=1,2...Z)
Kr(x, y)=A (x, y) × Ir(x, y) (r=1,2...Z)
Wherein Z is pendulous frequency, and the size of Z how much decision ciphertexts.
4. more image encryption methods based on compressed sensing relevance imaging according to claim 3, it is characterised in that the step
It is rapid 3) in decrypting process it is as follows：
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<mi>T</mi>
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<mi>min</mi>
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Br=∫ dxdyIr(x, y) M (x, y) (r=1 ..., Z)
In formula, Ti(x, y) represents plaintext to be decrypted, i.e. N width image information, and Z represents pendulous frequency, by TiThe square of (x, y) composition
Battle array is as sparse matrix to be asked；The combination K of the Z Stochastic Modulation signals being independently distributed and coding informationr(x, y) forms new square
Battle array, as calculation matrix；Z measured value BrThe matrix of composition, as measured value；Calculate TiThe minimum L1 of the matrix of (x, y) composition
Norm represents, so that it may obtains the cleartext information T of reconstructionCS。
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Cited By (5)
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| CN109858264A (en) * | 2019-01-22 | 2019-06-07 | 四川大学 | A kind of color image encipher-decipher method of the moulds resolutions of vectors such as score stochastic transformation domain |
| CN110348232A (en) * | 2019-06-21 | 2019-10-18 | 西安理工大学 | Use the optical image encryption method of the calculating ghost imaging of phase iterative algorithm |
| CN111738897A (en) * | 2020-05-29 | 2020-10-02 | 南京航空航天大学 | Multi-image multi-encryption method based on speckle decorrelation |
| CN113285797A (en) * | 2021-04-30 | 2021-08-20 | 四川大学 | Optical rotation domain multi-image encryption method based on compressed sensing and deep learning |
| CN115314602A (en) * | 2022-08-02 | 2022-11-08 | 上海理工大学 | Multi-image encryption system based on image scaling and associated imaging |
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109858264A (en) * | 2019-01-22 | 2019-06-07 | 四川大学 | A kind of color image encipher-decipher method of the moulds resolutions of vectors such as score stochastic transformation domain |
| CN110348232A (en) * | 2019-06-21 | 2019-10-18 | 西安理工大学 | Use the optical image encryption method of the calculating ghost imaging of phase iterative algorithm |
| CN111738897A (en) * | 2020-05-29 | 2020-10-02 | 南京航空航天大学 | Multi-image multi-encryption method based on speckle decorrelation |
| CN111738897B (en) * | 2020-05-29 | 2024-02-13 | 南京航空航天大学 | Multi-image multi-encryption method based on speckle decorrelation |
| CN113285797A (en) * | 2021-04-30 | 2021-08-20 | 四川大学 | Optical rotation domain multi-image encryption method based on compressed sensing and deep learning |
| CN115314602A (en) * | 2022-08-02 | 2022-11-08 | 上海理工大学 | Multi-image encryption system based on image scaling and associated imaging |
| CN115314602B (en) * | 2022-08-02 | 2023-05-09 | 上海理工大学 | Multi-image encryption method based on image scaling and associated imaging |
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Application publication date: 20180323 |