Hybrid Transforms Based Watermarking, Encryption and Compression of Bio-Medical Images

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Volume: 09 Issue: 09 | Sep 2022 www.irjet.net p-ISSN: 2395-0072

Hybrid Transforms Based Watermarking, Encryption and Compression of Bio-Medical Images

1M. Tech scholar, Dept. Of Systems and Signal Processing, JNTU-GV College of Engineering Vizianagaram(A), Andhra Pradesh, India.

2Associate Professor, Dept. of Electronics and Communication Engineering, JNTU-GV College of Engineering Vizianagaram (A), Andhra Pradesh, India. ***

Abstract – Inthispaper,wearediscussingabouttheHybrid transforms-basedwatermarking,encryptionandcompression of bio-medical images. The entire system is designed using MATLAB Software with the version of 2021a. Nowadays, Medical information plays an important role in medical diagnosis, transferring of that information should be done in an encrypted manner. Proposed novel Image Watermarking which is based on the Discrete Wavelet Transform (DWT), Hessenberg Decomposition (HD) and Singular Value Decomposition (SVD). Inverse Discrete Wavelet Transform (IDWT) used for retrieval of image. In this, watermark is embeddedintosub-bandscoefficient.Sub-bandcoefficientsare marked by adding a watermark of the same size of the three sub-bands.Comparisonofembeddingawatermarkatvertical (LH), horizontal (HL) and diagonal (HH) details. Here, Compression scheme applied on the watermarked image to reduce size of the data without losing its quality. AES encryption is used for security and proposed methodology analyzed on data sets of MRI of brain images and Retina images. The performance metric is analyzed after dewatermarkingwithdifferentordersofDaubechieswaveletsby using parameters like Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), Root Mean Square Error (RMSE) and Compression Ratio (CR).

Key

Words: Image Watermarking, DWT, HD, SVD, IDWT, Compression, AES Encryption

1.INTRODUCTION

Watermarking is a process of embedding information into a digital signal such as image, video, audio data to easily identify the ownership of the original data.Therefore,itwillbeembeddedinawaywhichmakesit difficult to be visualize with the human eye and to be removed.Ascomputersaremoreandmoreintegratedvia the network, the distribution of digital data is becoming faster,easier,andlessefforttomakeexactcopies.Oneofthe presentresearchareasistoprotectdigitalwatermarkinside theinformationsothatownershipofinformationwillnotbe claimed by the third party. The working domain of the watermarking is either spatial or frequency. Medical data exchangebetweenthedepartmentsisnotjusthospitals,but betweenhospitalsindifferentgeographicalareas. Medical

images require strict security measures. The medical information can often be shared to the professionals to improve treatment Huge amount medical data should be processed in hospitals for clinical and medical purpose of research. Malicious attacks on this medical data repots shouldbeavoidedandalsocancreatemedicalimageswith watermarks certification. Image quality is distorted in the spatialandtransformdomains.Theprocessofinsertingand extracting watermarking process. In the original image, a watermark(hiddenimageinformation)isinserted.IfImages areretrievedfromthedatabase,allowingphysicianstosee AuthenticationPhasewithExtractionwatermark.However, upon successful watermark extraction, created and the doctorcanproceedwiththediagnosistrust.Developmentof theDWT-HD-SVDAlgorithmBasedonthewatermarkused for this study A series of biomedical images are hidden under the cover image Embedding and retrieval of watermarks in data can be analyzed. This transformation includesfrequencyandLocationinformation,asopposedto theFouriertransform.Here,Signalenergyisconcentratedin specific wavelet coefficients with the discrete wavelet transform.Thispropertyisusefulwhenimagecompression is needed. lossless compression is used to Minimize data size. Encryption and decryption are Used the security. Proposal performance of medical data storage is also evaluatedbymultiplemetricssuchasmeansquarederror (MSE),peaksignalsignal-to-noiseratio(PSNR),root-meansquareerror(RMSE),andCompressionratio(CR).

2. LITERATURE SURVEY

A safer and more robust digital watermarking method based on encryption algorithms and asymmetric RSAEncryptiontechniqueshavebeenproposedtoprotect hidden things (DWT) and singular value decomposition (SVD) Thepowertoestimateembeddingawatermarkinthe low-frequency sub bands of the host image. Transmitted biomedical images and visual data are Watermark encryptionalgorithmanduseofthisalgorithmcompression, watermarking,Encryptiontoprotectdata.medicalimaging combined with security analysis of A study of encryption/watermarking (E/W) systems found that Wavelet transform (DWT) and singular values Decomposition Techniques to Reduce Ambiguity (SVD)

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International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056

Volume: 09 Issue: 09 | Sep 2022 www.irjet.net p-ISSN: 2395-0072

Guaranteedbysingularvaluedecomposition(SVD).Digital Watermarking Method Combining Discrete Wavelets Transformation by Singular Value Decomposition (DWT) Show that DWT-SVD produces good images uses block encryption techniques to achieve this. Watermark image encryption and compression, the method relies on watermarks. Distributors need to know each user`s encryption key to complete the process processing, managementanddistributionofprivatekeys;Errortryingto improve watermark security Watermark extraction takes time.proposedanewimagewatermarkingapproachbased on DWT-HD-SVD, AES encryption are trying to improve watermarksecurityandwatermarkextraction.

3. PRELIMINARIES

ThissectionpresentsthethreetechniquesDWT,HD,and SVD used in the proposed watermark method. MultiresolutiontimescalesignalofDWTcanimprovewatermark performance. HD performs as the matrix transforms have further improved robustness. In addition, SVD-based watermarking schemes have improved performance in defendingagainstgeometricattacks.

3.1 Discrete Wavelet Transform

TheDiscreteWaveletTransformisthetransformusedin bothNumericalandfunctionalanalysis.Waveletissampled. This transform uses discrete values. This conversion includes Advantages of collecting both frequency and locationinformation,ContrastwiththeFouriertransform. signal energy Focus on specific wavelet coefficients in discreteWavelettransform(DWT).Thispropertyisuseful forimagescompression.Amulti-resolutionrepresentation calledDWTusedtoDecodestepbystepfromlowresolution tohighresolution.DWTseparatesthesignalintotwohalves. HighFrequencyandlowfrequency.highfrequencysegment representstheedgecomponent,andthelowfrequencyisthe section and is again divided into a high frequency section and a low frequency section. Since the human eye is not sensitivetoedgevariations,Highfrequencycomponentsare usuallyused.

3.2Hessenberg Decomposition

HDisatypeofmatrixdecompositionscanbeusedfor squarematrixdecomposition.An×nsquarematrixXcanbe decomposedbyusingHDasshownby

PHPT =HD(X),

where P is an orthogonal matrix and H is an upper Hessenbergmatrix,andhi,j=0wheni>j+1.HDiscomputed by the Householder matrices. Householder matrix Q is an orthogonalmatrixanditisexpressedas

Q=(In −2µµT)/µT µ, where µ is a non-zero vector in Rn, and I n is a n × n identity matrix. n−2 steps are in the overall procedure. Therefore,HDiscomputedas P=(Q1Q2 ...Qn−2)TX(Q1Q2 ...Qn−2) ⇒H=PT XP ⇒X=PHPT

3.3 Singular Value Decomposition

Itisafactorizationofthatmatrixintothreematricesin linear algebra. It possesses some unique algebraic characteristics and communicates key geometrical and theoreticalinsightsintolineartransforms.Itisalsohaving several interesting data science applications. It is a signal processingtechniquethatiswidelyemployed.SVDisused for noise reduction and image compression, among their things.

A=U∑V

A isamxnmatrixthatyouobtainedfromanimageor anotherdatasource.Theorthogonalmatrices U and V are orthogonalmatricesand ∑ isadiagonalmatrix.Findingthe eigenvaluesandeigenvectorsof A and isthefirst stepincalculatingtheSVD.

The columns of V are formed by the eigenvectors of ,whilethecolumnsofUareformedbytheeigenvectors of A Singular values in S are also square roots of eigenvaluesfrom A or …singularvaluesarealways numbersthataregenuine.

Fig -1:DWTSub-bands

Here, LL band filter in 1-stage DWT decomposition contains a lot of information from the original image. Vertical, horizontal, skew(diagonal) information of the originalimagesisincludedintheLH,HL,andHHbands. LL bandOnlyimagescanreproducetheoriginalimage.Other bandsareignored.

Itisnormallyactsonthehostimage,orthehostimageis first segmented into numerous small blocks and then decomposedusingSVDtogeneratesingularvalues,which are used to incorporate watermark information. The SVD coeffects’sizeisconstant,thesingularvaluescandescribean image’sessentialalgebraicproperties,andsingularvalues are likely to vary substantially when the image is slightly disrupted.

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3.4 Encryption

Encryptionisessential toInternetsecuritytoday. The encryptionsystemtransformsandencryptssensitivedataas Code with mathematical calculations.only the correct key reveals the original data and make sure it's safe from everyoneExceptauthorizedparties.encryptionisHidedata bytransformingitintoasecretcode.

Data encryption and decryption research known as cryptography.Forcomputers,whatisplaintext?Ciphertext referstoencrypteddata,butunencrypteddata.

Therearethreemaintypesofencryptionsforsecurity, theseareAES,DES,andRSA.AdvancedEncryptionStandard (AES) is best Encryption standard. AES Encrypts data in single blocks rather than as individual bits. the Block size indicatesthenameofeachAEStypeofencrypteddata.Here, samekeyisusedforencryptionanddecryption.Decryption, althoughthetechniqueisimplementedSeparately.

Thisprocessinvolvesencryptionanddecrypt, butthe only difference is the decryption Implemented in reverse order.

=AES ( )

Where, isthepreviousencryptedblockand isthe encryptionkey.

3.5Compression

Imagecompressionisa techniquefordiminishingthe irrelevanceandredundancyofimagedatasothatitcanbe stored or transmitted in a more Efficient manner. Image compressionistheprocessofShrinkingthesizeofanimage withoutsacrificingitsquality.

Inafileandsendorcommunicatewithothers.Oneof the most widely used transform techniques for image compression of medical images using wavelets is Discrete Wavelet Transform (DWT). This DWT is very useful for compressing signal and shows better results for medical grayscaleimages.

While using DWT the important parameters that are taken into consideration are testing the image, wavelet function, number of iterations and calculation complexity. Thesewaveletstransformsareusedtoprocessandimprove signals in fields like medical imaging where image degradationisnottolerated.

4. PROPOSED WORK

Fig -2:Blockdiagramofproposedmethod

Thedatasetsconsideredinthisprojectareasetofretina imagesandMRIofbrainimages.

4.1 Watermark Embedding:

Here,wedesignanalgorithmfortheapplicationof thewatermark.watermark,encryption,andcompressionare representedinthisalgorithm.Intheprocessofwatermark embedding,twolevelDWTisappliedonthehostimageand thelowfrequencysubbandwasthenobtainedandsubband was further processed by HD and SVD. In addition, the scalingfactorisaddedtocontrolthewatermarkembedding strength.Thenewvaluewassubdividedonceagaintogeta newsingularvaluewhichwasusedtoreconstructfrequency sub band. Finally, the watermarked image was formed by makingusingofnewsubbandaftertheprocessofinverse discretewavelettransform.Afterthatapplythecompression on the watermarked image and the AES encryption is applied to the compressed image. The decryption and the decompressionareconductedseparately.

Algorithm for applying watermarking to an image:

1. Starttheprogram

2. Lookatthefirstimageintheinput(coverimage).

3. Open Dataset 2 (Host image) or image 2 (input image).

4. Resizetheimage1to300X300.

5. Resizetheimage2to300X300andgenerateakey valuetoapplywaveletsontheimages.

6. To partition the image for watermarking, use the DWTapproachusingthehaarwavelet.

7. AfterapplyingtheDWTtotheimage,itisseparated intofoursub-bands:h-LL,h-LH,h-HL,h-HH.

8. HDisperformedonLL,anditisshownasPHP T = HD(LL).

9. ExtracttheRGBcolorfrominputimage1andinput image 2, apply SVD algorithm on red, green, and bluecolorandSVDtoHi.e.,HUwHSwHVT w=SVD(H).

10. WisappliedwithSVD,i.e.,UwSwVT w =SVD(W).

11. Compute an embedded singular value HS∗ w by addingHSwandSwwithascalingfactorαbyHS∗ w= HSw +αSw

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12. The watermarked sub-band H∗ is generated by usingtheinverseSVD,i.e.,H∗ =HUwHS∗ wHVT w

13. Apply the watermarking with some intensity and gettheoutputimagewiththehelpofIDWT.

14. Save the watermarked image with the help of imwritecommand.

15. Applythecompressiononthewatermarkedimage and save the compressed image in a secret folder withthehelpofimwritecommand.

16. Apply the encryption to the compressed watermarkedimage.

4.2 Watermark Extraction:

TheoperationofDWTwasappliedtothewatermarked image, and the low frequency approximate coefficient of Which was further decomposed. The Watermark was obtainedundertheutilizationoftheoriginalhostimageand thenewlyformedsingularvalue.

Algorithm for de-watermarking:

1. Starttheprogram

2. NowReadthesecretfolder.

3. SavetheDecrypted&decompressedimagewiththe helpofimwritecommand.

4. Readthewatermarkedimage.

5. Apply the DWT technique with Haar wavelet to divide the image for watermarking. The image is then divided into 4 sub-bands i.e., ωm -LL, ωm-LH, ωm -HL,ωm-HH.

6. HDisperformedonLLw byPwHwPT w =HD(LLw) Apply SVD to Hw, i.e., HU∗ wHSb∗ wHV∗ w T = SVD(Hw) andExtracttheRGBcolorfromoriginalimageand applySVDalgorithmonred,green,andbluecolor. TheextractedsingularvalueS∗ w isgainedby S∗ w = (HSb∗ w − HS∗ w)/α.The extracted watermark W∗ is reconstructed by inverse SVD, which is describedbyW∗=Uw2S∗ wV-Tw2.

7. Apply the watermarking with some intensity and getoutputimagewithhelpofIDWT.

8. Save the extracted watermark with the help of imwritecommand.

9. CalculatetheparameterslikeMSE,PSNR,RMSEand CRofwatermarkedimage.

5. EXPERIMENTAL RESULTS

In this section, the performance measures were evaluated and proposed algorithm is evaluated using the available dataset. which are based on the TCGA and _test datasets is presented in this work. The TCGA dataset contains50images,_testdatasetcontains 25images.

Table -1: DatabaseDescription

S.No Dataset type

Sizeof dataset No.ofimages

1 TCGA 14MB 50

2 Test dataset 6MB 25

5.1 Analysis of algorithm

Manyofwatermarkingalgorithmsareunabletoprovide better qualityandimperceptibilityoftheimage have been subjected to performancemeasures.Toaddress thisissue, this work is to perform DWT-HD-SVD watermarking by comparingtheperformancemeasuresofthewatermarked image.

Watermarking when using Brain MRI and Retina images

Fig -5(a):Inputimagesforwatermarking

The cover image is at the left, at the same time as the watermarkimageisattheright,asshowninfig.5(a).When we apply the watermark embedding to the MRI of brain database all the images arehidden under cover image but earlierthanmakinguseofthewatermarkthepre-processing levelisinvolved.

Fig -5(b):inputimagesafterpreprocessing

Asillustratedinfig5(b),theinputimagesarerescaledtoa consistentsize.

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Mean Square Error (MSE):

Itistheaveragedvalueofthesquareofthepixel-by-pixel differencebetweentheoriginalimageandstego-imagewhich givesusameasureoftheerrorproducedinthatcoverimage byusingthedataembeddingprocess.

Fig -5(c):applicationofwaveletstoinputimages

The wavelets are applied to both the input image andthewatermarkedimageafterthepre-processingstage, dividingtheimagesintofoursub-bands:LL-1,LH-1,HL-1, HH-1,LL-2,LH-2,HL-2,andHH-2. Here, theLL sub-bands containingthemostinformation.

Fig -5(d): watermarkingandencryption

As illustrated in fig.5(d) the left image shows the watermarkedimageafterthewatermarkingphasetheimage issubjectedtocompressionandencryption.Theencrypted imagewillbelikeshownintheaboverightmostfigure.

WhereP,Qrepresentstheheightandwidthoftheimage respectively.

•m(i,j)representstheoriginalimagespixel valueand

•n(i,j)representsthepixelvaluesofembeddedimage.

Peak Signal to Noise Ratio (PSNR):

To find the watermarked image’s quality loss in comparison to the original image. The PSNR of an image affectsitsimperceptibility. PSNRiscalculatedbyusingthe formulaasfollows.

PSNR = 10 log10 = (L*L/MSE)

Here,Listhevalueoftheimageheight.For8-bitimage, L=255.Anyimagewithabrightnessofgreaterthan30DBis acceptable in the application of multimedia. In medical imagingthedataqualityisparamount,andPSNRofaround 50Dbindicatesthattheimageisofhighqualityandthatthere hasbeennosignificantdegradationintheimage comparedtotheoriginal.

Root Mean Square Error (PMSE):

ThesquaredrootofMSEisusedtocalculateRMSE.Itisa metricusedtoindicatehowmuchapixelchangesinmeansof processing. Because RMSE is dependent of scale, it should onlyusetoevaluatethepredictionerrorsofdifferentmodels forasinglevariable,notbetweenthevariables.

Fig -5(e): Decryptedandextractedimage

The decrypted image is generated by applying the decryptiontothecompressedimage,asshowninfig.5(e).The extractedwatermarkedimageispresentedontheright.By applying the De-watermarking, the Brain MRI image was retrievedfromthewatermarkedimage.

Image quality measurement:

Somequalityofmeasurescanbeusedtodetermine thedistortioninthewatermarkedimagebycomparingitto theoriginalimage.Thefollowingsectionsarewidelyused.

Where, f=forecasts (unknown results) 0=observed values (knownresults)

Compression Ratio (CR):

The squared root of MSE is used to calculate the Root Mean Square Error (RMSE). The Root Mean Square Error Imagecompressionistheprocessofreducingthesizeofan image file in bytes without sacrificing the images’ quality. Moreimagescanbesavedineachamountofdiscormemory spacebecauseofthesmallerfilesize.

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CR=n1/n2

Where,letn1,n2denotethenumberofbitsintheoriginal andcompressedimages

Fig -5(f): PerformancemeasuresfortheMRIDatabase

Thisfigureshowstheperformancemeasuresandgraph fortheBrainMRIdatabaseandthetimetakenfortheentire watermarking and de-watermarking is around 5 sec. The encryptedandcompressedimagesarestoredinaseparate folder.

Chart -2: MSEforBrainMRIfordifferenttypesofordersof dBfilters

ThisMSEfigureshowstheresultfortheMSEvaluesofthe Brain MRI dataset for different orders of the Daubechies wavelet filters. The Mean Square Error (MSE) is used to determinethedegradationofimage.

Chart -1: PerformancemetricesfortheBrainMRIDataset usingthedb1waveletfilter

Chart -3: PSNRforbrainMRIfordifferenttypesoforders ofdBwaveletfilters

This figure showsthe results for PSNR values of the Brain MRIdatasetfordifferentordersoftheDaubechieswavelet filters.Todeterminetheimperceptibility,PSNRiscalculated totheextractionofwatermarkedimages.

6. CONCLUSIONS

Inthispaper,theproposedimagewatermarkingsystem is based on DWT-HD-SVD transforms. The invisibility and robustness of this method are analyzed by the numerical simulationtrailsandtheresultsshownthebetterquality.

In addition, the comparison with the related works is listed and the corresponding metric values show that the proposedmethodhasabetterperformancewhichisusedto securetheimagestransferredviatelemedicineasmuchas possible.Theseimagesarewatermarkedwiththepatient’s

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details to avoid any mistake between the patient’s radiographs. As a result, during the extraction, the doctor willbeabletocheckwithcertaintythatthereportsbelongto thetreatedpatient.

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