CN107947919A - The compressed sensing relevance imaging encryption method of large information capacity image based on QR codes - Google Patents
The compressed sensing relevance imaging encryption method of large information capacity image based on QR codes Download PDFInfo
- Publication number
- CN107947919A CN107947919A CN201711121429.6A CN201711121429A CN107947919A CN 107947919 A CN107947919 A CN 107947919A CN 201711121429 A CN201711121429 A CN 201711121429A CN 107947919 A CN107947919 A CN 107947919A
- Authority
- CN
- China
- Prior art keywords
- image
- mrow
- codes
- information
- comentropy
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
- H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
- H04N19/102—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the element, parameter or selection affected or controlled by the adaptive coding
- H04N19/119—Adaptive subdivision aspects, e.g. subdivision of a picture into rectangular or non-rectangular coding blocks
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L63/00—Network architectures or network communication protocols for network security
- H04L63/04—Network architectures or network communication protocols for network security for providing a confidential data exchange among entities communicating through data packet networks
- H04L63/0428—Network architectures or network communication protocols for network security for providing a confidential data exchange among entities communicating through data packet networks wherein the data content is protected, e.g. by encrypting or encapsulating the payload
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L9/00—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols
- H04L9/08—Key distribution or management, e.g. generation, sharing or updating, of cryptographic keys or passwords
- H04L9/0816—Key establishment, i.e. cryptographic processes or cryptographic protocols whereby a shared secret becomes available to two or more parties, for subsequent use
- H04L9/085—Secret sharing or secret splitting, e.g. threshold schemes
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L9/00—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols
- H04L9/08—Key distribution or management, e.g. generation, sharing or updating, of cryptographic keys or passwords
- H04L9/0861—Generation of secret information including derivation or calculation of cryptographic keys or passwords
- H04L9/0869—Generation of secret information including derivation or calculation of cryptographic keys or passwords involving random numbers or seeds
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
- H04N19/46—Embedding additional information in the video signal during the compression process
- H04N19/463—Embedding additional information in the video signal during the compression process by compressing encoding parameters before transmission
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
- H04N19/90—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using coding techniques not provided for in groups H04N19/10-H04N19/85, e.g. fractals
- H04N19/91—Entropy coding, e.g. variable length coding [VLC] or arithmetic coding
Landscapes
- Engineering & Computer Science (AREA)
- Signal Processing (AREA)
- Computer Security & Cryptography (AREA)
- Multimedia (AREA)
- Computer Networks & Wireless Communication (AREA)
- Computer Hardware Design (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Compression Of Band Width Or Redundancy In Fax (AREA)
- Compression Or Coding Systems Of Tv Signals (AREA)
Abstract
The present invention relates to a kind of compressed sensing relevance imaging encryption method of the large information capacity image based on QR codes, calculates the comentropy of original image, is compressed by judging whether comentropy is more than 4bit/ pixels to determine whether needing progress QR to encode with QR codes；Association encryption：QR codes image after the low original image of comentropy or compression and key are associated imaging and obtain ciphertext；In message transmitting procedure, transmission side and recipient share the key, and the ciphertext is transmitted between both sides；Key and ciphertext are compressed perceptual computing during decryption, reconstruct cleartext information, QR decodings are carried out to the QR codes image reconstructed, obtain undistorted image.Calculated and screened using the comentropy of image, avoid QR Coding Effects in low comentropy image；The image high to comentropy carries out QR codings and compression, shortens reconstitution time, and ensures that original image information is not destroyed in compression process, and can be in the final decoding undistorted image identical with original image.
Description
Technical field
The present invention relates to a kind of image encryption technology, the compression sense of more particularly to a kind of large information capacity image based on QR codes
Know relevance imaging encryption method.
Background technology
Relevance imaging technology can be adapted for any light source, such as fluorescent lamp, laser even sunlight.This technology can be kept away
Exempt from the environmental disturbances of dense fog, sleety weather, so as to obtain more clearly image.But because of the characteristic of relevance imaging, it is imaged speed
Degree is slow, and imaging size is extremely limited.Reality using the relevance imaging algorithm based on compressed sensing to relevance imaging system
Test data and carry out data processing, can solve relevance imaging experimental study with the reduction system imaging detection times of high degree
In difficulty, but this technology can not solve the difficulty in the larger image relevance imaging of information content very well.For information content
Big object to be imaged, compressed sensing relevance imaging still remain the problems such as image taking speed is slow, and data processing overheads are big.
QR codes are the abbreviations of quick recognition matrix code, belong to matrix type two-dimension bar code.QR codes have very high recognition
Speed, very high information capacity and compressibility, it is crucial that QR codes have very strong error margins.
The content of the invention
The present invention be directed to the difficult problem in the larger image relevance imaging of information content, it is proposed that a kind of based on QR codes
The compressed sensing relevance imaging encryption method of large information capacity image, can realize the relevance imaging of the larger image of information content.
The technical scheme is that：A kind of compressed sensing relevance imaging encryption side of the large information capacity image based on QR codes
Method, specifically comprises the following steps：
1) classify：The comentropy of original image is calculated, judges whether comentropy is more than 4bit/ pixels, as image information exceedes
2) 4bit/ pixels then enter step, if image information is not less than 4bit/ pixels, then enter step 4)；
2) QR is encoded：QR codings are carried out to image, form a QR code；
3) compress：The QR codes of formation are compressed into the small QR codes image that pixel size is 50 × 50；
4) association encryption：Using the image that the unpressed image of step 1) or step 3) are compressed as relevance imaging plaintext into
Row encrypted transmission, is associated imaging by key and the plaintext and obtains a series of encrypted information, key for it is a series of with
Machine modulated signal, encrypted information, that is, ciphertext；
5) transmit：In message transmitting procedure, transmission side and recipient share the key, are transmitted between both sides described close
Text；
6) decrypt：Key and ciphertext are compressed perceptual computing during decryption, reconstruct cleartext information, in this way uncompressed figure
Encrypted as being associated imaging, it is original image after reconstructing to reconstruct cleartext information, is added as being associated to be imaged for compression image
Close, obtained cleartext information is the QR code images of reconstruct, is entered step 7) after decryption；7) QR is decoded：To the QR codes reconstructed
Image carries out QR decodings, obtains the undistorted image identical with original image.
The calculation formula of the comentropy of image is as follows in the step 1)：
Wherein pij=f (i, j)/N2
I represents the gray value of pixel, and 0≤i≤255, j represent neighborhood gray average, 0≤j≤255.
The encrypted specific calculating of step 4) association is as follows：
By the signal I of Stochastic Modulationr(x, y) is used as key, is multiplied with plaintext T (x, y) to be encrypted, obtains ciphertext Br, it is close
Text is obtained by following formula,
Br=∫ T (x, y) Ir(x,y)dxdy (3)
Wherein Ir(x, y)=| Er(x,y)|2
Er(x, y) is the light distribution of speckle.
Key and ciphertext are compressed perceptual computing during decryption decryption in the step 6), it is specific as follows：
Br=∫ T (x, y) Ir(x, y) dxdy, r=1 ... N
WhereinRepresent 1- norms, TCSRepresent the information of reconstructed image, N is sampling number.
The beneficial effects of the present invention are：The compressed sensing relevance imaging of large information capacity image of the invention based on QR codes adds
Decryption method, is calculated using the comentropy of image and is screened, avoid QR Coding Effects in low comentropy image, and can filter out
High comentropy image, reduces its comentropy；The image high to comentropy carries out QR codings and the compression of QR coded images, shortens weight
The structure time, ensures that original image information is not destroyed in compression process, and can be in the final decoding nothing identical with original image
Distorted image.
Brief description of the drawings
Fig. 1 is the method for the present invention flow chart；
Fig. 2 a are original image in the method for the present invention embodiment operational process；
Fig. 2 b are the QR codes generated in the method for the present invention embodiment operational process；
Fig. 2 c are compressed QR codes in the method for the present invention embodiment operational process；
Fig. 2 d are the QR codes that compressed sensing relevance imaging reconstructs in the method for the present invention embodiment operational process；
Fig. 2 e are the decoded images of QR in the method for the present invention embodiment operational process.
Embodiment
The compressed sensing relevance imaging encryption method flow chart of large information capacity image based on QR codes as shown in Figure 1, specifically
Include the following steps：
1st, classify：Original image is the gray level image of pixel size 256 × 256 as shown in Figure 2 a, calculates the information of original image
Entropy, judges whether comentropy is more than threshold condition, i.e. 4bit/ pixels.2 are entered step if image information is more than 4bit/ pixels,
If image information is not less than 4bit/ pixels, then 4 are entered step；Since Fig. 2 a comentropies are more than 4bit/ pixels, 2 are entered step；
The calculating of image information entropy is referring to formula (1)：
Wherein pij=f (i, j)/N2 (2)
I represents the gray value (0≤i≤255) of pixel, and j represents neighborhood gray average (0≤j≤255)；
2nd, QR is encoded：QR codings are carried out to image, form a QR code, as shown in Figure 2 b；
3rd, compress：The QR codes of formation are compressed into the small QR codes image that pixel size is 50 × 50, as shown in Figure 2 c；
4th, association encryption：Carried out the image that the unpressed image of step 1 or step 3 compress as the plaintext of relevance imaging
Encrypted transmission, is associated imaging by key and the plaintext and obtains a series of encrypted information, key is a series of random
Modulated signal, encrypted information, that is, ciphertext；
By the signal I of Stochastic Modulationr(x, y) is used as key, is multiplied with plaintext T (x, y) to be encrypted, obtains ciphertext Br, it is close
Text is obtained by formula (3).
Br=∫ T (x, y) Ir(x,y)dxdy (3)
Wherein Ir(x, y)=| Er(x,y)|2
Er(x, y) is the light distribution of speckle.
5th, transmit：In message transmitting procedure, transmission side and recipient share the key, are transmitted between both sides described close
Text；
6th, decrypt：Key and ciphertext are compressed perceptual computing during decryption, reconstruct cleartext information, in this way uncompressed figure
Encrypted as being associated imaging, it is original image after reconstructing to reconstruct cleartext information, is added as being associated to be imaged for compression image
Close, obtained cleartext information is the QR codes of reconstruct, is entered step after decryption 7), such as the QR codes that Fig. 2 d are reconstruct；
Br=∫ T (x, y) Ir(x, y) dxdy, r=1 ... N
WhereinRepresent 1- norms, TCSRepresent the information of reconstructed image, N is sampling number；
7th, QR is decoded：QR decodings are carried out to the QR codes image reconstructed, obtain the undistorted image identical with original image, such as
Fig. 2 e are the decoded images of QR.
The present invention calculating to the information content of digital picture using information entropy theory, and set a 4bit/ pixel
Information entropy threshold.Therefore two classes are divided the image into：One kind is to be less than the image of the threshold value for comentropy, is not transformed into
QR codes, are directly compressed and perceive relevance imaging；It is another kind of be for comentropy exceed the threshold value image, we by its
Be encoded to QR code of the comentropy size for 4bit/ pixels or so, i.e., can be by the higher image pressure of comentropy with QR codings
It is condensed to the QR codes of 4bit/ pixels or so.A threshold condition is so set, all gray level images are divided into two classes and are handled respectively,
Not only QR Coding Effects had been avoided in low comentropy image, but also high comentropy image can be filtered out, have reduced its comentropy.
The present invention has carried out compression processing to the QR codes of generation, is reduced into the small QR that a pixel size is 50 × 50
Code image, the size that this processing have compressed image as far as possible make the sampling number for which reducing relevance imaging, shorten reconstruct
Time, ensures that original image information is not destroyed in compression process, and can be in final decoding no mistake identical with original image
True image.
Plaintext of the present invention using QR codes as compressed sensing relevance imaging.General relevance imaging is larger for information content
Image, because association operand is big, sampling number is excessive, so restructuring procedure is slower, while the clarity of reconstructed image is not high.And
QR codes is lower as the sample rate needed for plaintext, and reconstitution time is shorter.Since QR codes have certain error-correcting performance.Therefore exist
During encrypted transmission, when QR codes are subject to the interference attack of certain noise, the QR codes that are reconstructed by compressed sensing relevance imaging
It can still decode, and the undistorted image identical with original image finally obtained.
Claims (4)
- A kind of 1. compressed sensing relevance imaging encryption method of the large information capacity image based on QR codes, it is characterised in that specific bag Include following steps：1) classify：The comentropy of original image is calculated, judges whether comentropy is more than 4bit/ pixels, if image information is more than 4bit/ 2) pixel then enters step, if image information is not less than 4bit/ pixels, then enter step 4)；2) QR is encoded：QR codings are carried out to image, form a QR code；3) compress：The QR codes of formation are compressed into the small QR codes image that pixel size is 50 × 50；4) association encryption：The image that the unpressed image of step 1) or step 3) are compressed is added as the plaintext of relevance imaging Close transmission, is associated imaging by key and the plaintext and obtains a series of encrypted information, key is adjusted at random to be a series of Signal processed, encrypted information, that is, ciphertext；5) transmit：In message transmitting procedure, transmission side and recipient share the key, and the ciphertext is transmitted between both sides；6) decrypt：Key and ciphertext are compressed perceptual computing during decryption, reconstruct cleartext information, in this way uncompressed image into Row relevance imaging is encrypted, reconstruct cleartext information for reconstruct after original image, such as compression image be associated imaging it is encrypted, Obtained cleartext information is the QR code images of reconstruct, is entered step 7) after decryption；7) QR is decoded：QR decodings are carried out to the QR codes image reconstructed, obtain the undistorted image identical with original image.
- 2. the compressed sensing relevance imaging encryption method of the large information capacity image based on QR codes according to claim 1, it is special Sign is that the calculation formula of the comentropy of image is as follows in the step 1)：<mrow> <mi>H</mi> <mo>=</mo> <munderover> <mo>&Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>0</mn> </mrow> <mn>255</mn> </munderover> <msub> <mi>p</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> <mi>log</mi> <mi> </mi> <msub> <mi>p</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> </mrow>Wherein pij=f (i, j)/N2I represents the gray value of pixel, and 0≤i≤255, j represent neighborhood gray average, 0≤j≤255.
- 3. the compressed sensing relevance imaging encryption method of the large information capacity image based on QR codes according to claim 1, it is special Sign is that the encrypted specific calculating of step 4) association is as follows：By the signal I of Stochastic Modulationr(x, y) is used as key, is multiplied with plaintext T (x, y) to be encrypted, obtains ciphertext Br, ciphertext by Following formula obtains,Br=∫ T (x, y) Ir(x,y)dxdy (3)Wherein Ir(x, y)=| Er(x,y)|2Er(x, y) is the light distribution of speckle.
- 4. the compressed sensing relevance imaging encryption method of the large information capacity image based on QR codes according to claim 3, it is special Sign is, key and ciphertext are compressed perceptual computing during decryption decryption in the step 6), specific as follows：<mrow> <msub> <mi>T</mi> <mrow> <mi>c</mi> <mi>s</mi> </mrow> </msub> <mo>=</mo> <mi>T</mi> <mo>;</mo> <mi>m</mi> <mi>i</mi> <mi>n</mi> <mo>|</mo> <mo>|</mo> <mi>T</mi> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> <mo>|</mo> <msub> <mo>|</mo> <msub> <mi>L</mi> <mn>1</mn> </msub> </msub> <mi>s</mi> <mi>u</mi> <mi>b</mi> <mi>j</mi> <mi>e</mi> <mi>c</mi> <mi>t</mi> <mi> </mi> <mi>t</mi> <mi>o</mi> <mo>:</mo> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>4</mn> <mo>)</mo> </mrow> </mrow>Br=∫ T (x, y) Ir(x, y) dxdy, r=1 ... NWhereinRepresent 1- norms, TCSRepresent the information of reconstructed image, N is sampling number.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711121429.6A CN107947919B (en) | 2017-11-14 | 2017-11-14 | Compressed sensing correlation imaging encryption method for large-information-volume images based on QR (quick response) codes |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711121429.6A CN107947919B (en) | 2017-11-14 | 2017-11-14 | Compressed sensing correlation imaging encryption method for large-information-volume images based on QR (quick response) codes |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN107947919A true CN107947919A (en) | 2018-04-20 |
| CN107947919B CN107947919B (en) | 2020-06-23 |
Family
ID=61933988
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201711121429.6A Active CN107947919B (en) | 2017-11-14 | 2017-11-14 | Compressed sensing correlation imaging encryption method for large-information-volume images based on QR (quick response) codes |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107947919B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113918969A (en) * | 2021-09-28 | 2022-01-11 | 厦门市美亚柏科信息股份有限公司 | Method for searching Bitlocker decryption key based on memory data |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103428399A (en) * | 2012-06-29 | 2013-12-04 | 上海理工大学 | Compressive sensing theory-based correlated imaging optical encryption method |
| CN104281866A (en) * | 2013-07-09 | 2015-01-14 | 航天信息股份有限公司 | Two-dimensional code application method and device |
| CN106530206A (en) * | 2016-11-15 | 2017-03-22 | 深圳大学 | Image encryption and decryption methods and image encryption and decryption devices based on optical encryption and decryption technologies |
-
2017
- 2017-11-14 CN CN201711121429.6A patent/CN107947919B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103428399A (en) * | 2012-06-29 | 2013-12-04 | 上海理工大学 | Compressive sensing theory-based correlated imaging optical encryption method |
| CN104281866A (en) * | 2013-07-09 | 2015-01-14 | 航天信息股份有限公司 | Two-dimensional code application method and device |
| CN106530206A (en) * | 2016-11-15 | 2017-03-22 | 深圳大学 | Image encryption and decryption methods and image encryption and decryption devices based on optical encryption and decryption technologies |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113918969A (en) * | 2021-09-28 | 2022-01-11 | 厦门市美亚柏科信息股份有限公司 | Method for searching Bitlocker decryption key based on memory data |
Also Published As
| Publication number | Publication date |
|---|---|
| CN107947919B (en) | 2020-06-23 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US9462152B2 (en) | System and method for hiding reversible information | |
| CN108009975A (en) | Jpeg image reversible information hidden method based on two-dimensional histogram modification | |
| CN101080013A (en) | A JPEG lossless compression image hide writing method based on predictive coding | |
| Zhang et al. | Novel video steganography algorithm based on secret sharing and error-correcting code for H. 264/AVC | |
| KR0181030B1 (en) | Apparatus for transmitting bitplane compressed by means of binary image | |
| CN105163122A (en) | Image compression and decompression method based on similarity of image blocks | |
| Favorskaya et al. | Robust textual watermarking for high resolution videos based on Code-128 barcoding and DWT | |
| US8228993B2 (en) | System and method for encoding and decoding information in digital signal content | |
| CN107947919A (en) | The compressed sensing relevance imaging encryption method of large information capacity image based on QR codes | |
| CN109785218B (en) | QR code-based physical domain image steganography method and device | |
| Xu et al. | JPEG compression immune steganography using wavelet transform | |
| Lin et al. | A reversible data hiding scheme for block truncation compressions based on histogram modification | |
| KR0181048B1 (en) | Apparatus for transmitting bitplane compressed by means of triangle block | |
| Chang et al. | Reversible steganography for BTC-compressed images | |
| CN103974080B (en) | Transmission error code correction method for image prediction compression coding | |
| JP4227425B2 (en) | Information processing method and apparatus, computer program, and computer-readable storage medium | |
| ShuangKui et al. | A Modification‐Free Steganography Method Based on Image Information Entropy | |
| Pandian | An Image steganography algorithm using huffman and interpixel difference encoding | |
| CN106375763B (en) | A kind of video encrypting/deciphering method and its encrypting and deciphering system based on Slepian-Wolf technology | |
| Patel et al. | Uncompressed Image Steganography using BPCS: Survey and Analysis | |
| Kaushik et al. | A two stage hybrid model for image encryption and compression to enhance security and efficiency | |
| CN116567155A (en) | Safe and stable paper document transmission and reading method and system | |
| Yan et al. | Partial encryption of JPEG2000 images based on EBCOT | |
| Cheng et al. | A reversible JPEG-to-JPEG data hiding technique | |
| Gopinathan et al. | A study of image compression and SHA 256 encryption algorithms for secure transmission |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |