A Fuzzy Logic based Bit Plane ComplexitySegmentation steganography to secure Electronic Health RecordsB.
AdithyaM.Tech(Information Technology)PondicherryEngineering [email protected] Abstract— Electronic Healthcare or E-Healthcare (EH) is apaperless management promises to speed up typical bureaucracy of healthcare.
Atypical E-Healthcare system consists of many component and subsystem, such asappointment, routine clinical notes, picture archiving, lab and radiologyorders, etc., are vulnerable to security threats. Cryptology and steganographyare generally used to ensure medical data security. For this reason, a FuzzyLogic based Bit Plane Complexity Segmentation (FL-BPCS) steganography is combinedwith AES cryptography and Huffman lossless compression is proposed to securepatient data’s.
In this, Electroencephalogram time series, doctor’s comment andpatient information are selected as hidden data and Magnetic Resonance imageare used as cover image. Keywords—E-Healthcare,steganography, FL-BPCS, Electroencephalogram, cryptology I. INTRODUCTIONThe privacy andsecurity of EH information falls into two categories. First, inappropriarereleases from authorized users who intentionally or unintentionally access ordisseminare information violation of EH system policies or EH computer systemcan be break by outsiders. Second, open disclosure of patient healthinformation to parties against to the interests of specific individual patientor invadere a patient’sprivacy.These falls arises from the flow of data across the EH system amongst andbetween providers, payers and secondary users, with or without the patient’sknowledge are conceptually quite differ or require different counteracts andinterventions.
Technicalobstaculum of the intrudere includes the use of firewalls to isolate internalnetworks with strong encryption based authentication and authorization. Thereis no known obstaculum for external networks like Denial-of-Service. Nevertheless,technical countermeasures cannot be cures all security threats. Obstaculum suchas encryption, steganography and authentication are the only effective ways tocounter EH security threats against internet interface. G. SanthiAssistant Professor (Information Technology)PondicherryEngineering [email protected] Steganography isused for covert communication 2. The embedding algorithm will convert thecover medium into stego medium by embedding secret data into it.
The inverseprocess of embedding is done to extract the secret data. Imperceptibility,security, capacity, robustness, embedding complexity are the steganographyfactors that has to be considered. Image steganography is developed accordingto its use in medical fields to communicate between patient as well ascommunication between doctor’s and laboratory people’s to hide secret messages.
Steganography is to avoid drawing attention to the transmission of hiddenmedical information. If suspicion is raised, steganography and cryptography isplanned to achieve the security of secret medical data’s. Both arecomplementary to each other and provide better security, confidentiality andauthenticity.Eiji Kawaguchiet al. proposed a Bit Plane Complexity Segmentation (BPCS) to increase theembedding capacity and also to overcome the short comes of traditionalsteganography techniques such as Least Significant Bit (LSB), Transformembedding, Perceptual masking techniques. But the issue is embedded secret datacan be retrieved by using Difference Image Histogram (DIH) 7.
Karakis et al.proposed a Fuzzy Logic based Least Significant Bit (FL-LSB) to reduce theinsecurity of LSB planes. By using fuzzy the LSB planes were chosen to embedthe secret data’s. But the issue, is low embedding capacity and the stego imageis invalid 8. This technique can be offered by using the following sections.
In section 2, the brief description of BPCS, cryptology as well as lossless compressiontechnique is presented, in section 3, the work methodology is presented, insection 4, results are presented, in section 5, the discussion and analysis aredone, and finally the work is concluded. II. BRIEFDESCRIPTION OF BPCS, CRYPTOLOGY AND LOSSLESS COMPRESSION TECHNIQUE A.Bit Plane Complexity Segmentation for embed and extract Thissteganography method makes use of the human vision. The cover image is dividedinto informative region and noise-like region and the secret data is hidden innoise blocks of vessel image without degrading image quality. The data ishidden in both Most Significant Bit (MSB) as well as LSB planes 7. B.Cryptologywith security concern Cryptology usesthe Advanced Encryption Standard (AES) to encrypt the secret data to formsecond security layer.
It comprises of a series of linked operations, some ofwhich involve replacing the inputs by specific outputs (Substitution) andothers involve shuffling bits around (Permutations). It treats the 128 bits ofa plaintext block as 16 bytes as a matrix. It has built-in flexibility of keylength, which allows a degree of future-proofing against progress in theability to perform exhaustive-key searches 4.
C.Losslesscompression technique to reduce the size Losslesscompression technique uses the Huffman to reduce the secret data size. Thismethod takes a symbol (bytes) and encodes them with variable length codesaccording to the statistical probabilities. A frequently used symbol will beencoded with couple of bits, while symbol that are rarely used will be encodedwith more bits 1. III. PROPOSEDWORKThe proposed system aims to use EEGsignals and MR images that are obtained from same patients and to embed moredata with EEG into MR images of same patient. For this reason, the MR imagesand EEG of epilepsy patients are gathered from the Department of Neurology atBonn University.
12 females and 11 males were included (age: 18-65 years; meanage: 35 ± 7.7 years). The embedded message was combined with the patient’s information,doctor’s comments, and EEG time series from the EEG file header. The patient’sinformation (patient name, patient ID, patient birth date, patient gender,patient age, patient weight, patient address, study description, series date,series time, and series description) were separately selected from themeta-header of each of the DICOM files.
Least Significant Bit (LSB) and MostSignificant Bit (MSB) embedding is a simple and fast strategy in steganography.It has high imperceptibility and embedding capability. Hence, this studyproposes new methods to modifyLSB and MSB embedding using medical data. The analyses consist of two stages:embedding and extracting, respectively. Initially, the patient’s information isobtained from DICOM series of epilepsy patient. The EEG data is segmentedaccording to the size of these DICOM images.An image is sampled by pixels.
In thegray-scale image, pixels have gray level intensities. In color images, pixelsare also represented by three component intensities, being red (R), green (G),and blue(B). A similarity measure is the similarity degree between two groupsor between two objects. In image processing, the similarity measure of two pixelsis used with distance information in Euclidean color space. Demirci 6proposed a similarity-based method for edge detection. Furthermore, Pixel-ValueDifferencing (PVD) or Adjacent Pixel Difference (APD) methods determineembedding pixels in histogram-based steganography.
These methods have highembedding capacity and PSNR values. The main idea of this method is togenerate a new image whose pixels have double values at the interval 0 1.This similar image of cover image is used to determine pixels for the embeddingmessage. In this method, if the values of similar pixels are higher than thedetermined threshold by trial and error, they are selected to hide the message8. The neighboring pixels of the image (P1,P2,…
P9) using the 3×3 windowhave three color component (R, G, B).The gray leveldifferences of color components are calculated the neighboring pixels of thestego image. The color distance of pixels are calculated by the Euclidean norm8.
Thesimilarity values of pixels are founded. Similarly, the coordinates ofthe pixels are determined between the similarity values of pixels and thresholdvalues. The hidden message is extracted using the coordinates of the stegoimage’s pixels. Fig. 1 Data flow of theoverall proposed work In message pre-processing stage,lossless compression techniques, which are LZW (Lempel–Ziv–Welch) and HuffmanCompression, are used to increase message capacity. Furthermore, LZW andHuffman Compression methods also ensure the complexity of the message.
To increasesecurity, the compression message is encrypted by the Rijndael symmetricencryption algorithm using a 128-bit key. Secondly, the proposed methods, whichare Fuzzy-Logic-based Bit Plane Complexity Segmentation (FL-BPCS),select MSBs and LSBs of image pixels with using the differences in gray levelsof the pixels. Finally, the selected MSBs and LSBs of the pixels are alteredwith the message bits in stego images. These processes are simultaneously runwith all DICOM series to decrease computational time.
The extracting messagestage requires stego-DICOM images and a stego-key is shown in fig. 1, which isthe authentication key for decryption. Firstly, the proposed methods give thepixels coordinates, which have an embedded message. These pixels are used togather the message. Secondly, the obtained message is decrypted anddecompressed. Finally, the patient’s information, segmented EEG, and thedoctor’s comments are displayed in a GUI (Graphical User Interface) screen isshown in fig. 2,3. All hidden EEG data can be also gathered from the DICOMseries.
The comparison results of the proposed algorithm are evaluated by PSNR(peak signal-to-noise ratio), MSE (mean square of error), SSIM (structuralsimilarity measure), between the cover, and the stego-DICOM series. IV. RESULTSThe proposed system parametersare used to evaluate the performance of the data hiding techniques.Peak Signal to Noise Ratio (PSNR): The PSNR is generally usedto measure the quality of stego image in decibels (dB). Eq.1, gives the expression forPSNR in which ICmax is the maximum pixel value of the coverimage and MSE is the mean square error: (1) Where, (2) In Eq. 2, x and y are the image coordinates, M and N arethe dimensions of the image, ISxy is the generated stego-imageand ICxy is the cover image 3,9.
Structural similarity (SSIM) index: The SSIM is a method for findingthe similarity between cover image and the stego image. It is aperception-based model that considers image degradation as perceived change instructural information. The SSIM measure between two images IC andIS is represented in Eq. 3, where, isthe average of IC, isthe average of isthe variance of isthe variance of isthe covariance between IC and IS and k1,k2 are two the variables used to stabilize the division withweak denominator 5,9.
(3) Fig. 2: Cover image andstego imageFig. 3: Histogram ofcover image and stego image V.
DISCUSSIONAND ANALYSISThegraph is created based on the embedding capacity is shown in fig. 4. In FuzzyLogic based Least Significant Bit the embedding capacity is mentioned as low,because to embed the secret data it occupies only the LSB positions. In BitPlane Complexity Segmentation the embedding capacity is mentioned as low, whencompared to the Fuzzy Logic based Least Significant Bit but normally theembedding capacity is high when compared to the primitive LSB because to embedthe secret data it occupies both MSB as well as LSB. In Fuzzy Logic based BitPlane Complexity Segmentation the embedding capacity is high because to embedthe secret data the red, green, blue channel were used with fuzzy. Fig.
4:Embedding capacityThegraph is created based on the performance evaluation parameters PSNR, MSE,SSIM is shown in fig. 5. In Fuzzy Logic based Least Significant Bit the PSNRis achieved high, because to embed the secret data it occupies only the LSBpositions and MSE is achieved low when compared to Bit Plane ComplexitySegmentation and SSIM is achieved high. In Bit Plane Complexity Segmentationthe PSNR is achieved as low because to embed the secret data it occupies bothMSB as well as LSB and MSE is achieved high and SSIM is low because MSB is moresensitive.
While changing that pixel values with another pixel values must bematched. In Fuzzy Logic based Bit Plane Complexity Segmentation the PSNR is lowbecause to embed the secret data MSB as well as LSB is used. MSE is low andSSIM is high because it uses red, green, blue channels with fuzzy rules. Fig.
5: Performance Parameters Thegraph is created based on the steganalysis is shown in fig. 6. Visual attacks involve observingthe unusual patterns and noisy blurred regions in some places of the stegoimage. A statistical methodcalled RS steganalysis for detection of LSB embedding uses dual statisticsderived from spatial correlation of an image. Histogram based steganalysis techniques detect the existence ofsecret data from smoothness of the stego image histogram.
Similarly, a targetedactive steganalysis technique is implemented for HS embedding using the changein the characteristics of histogram during data embedding 9. InFuzzy Logic based Least Significant Bit the visual attacks, RS statisticalattack, Sample Pair Analysis is achieved high, because to embed the secret data it occupies only the LSBpositions but Difference Image Histogram is achieved low. In Bit PlaneComplexity Segmentation the visual attacks, RS statistical attack, Sample PairAnalysis is achieved low, because to embed the secret data it occupies both MSBas well as LSB but Difference Image Histogram is achieved high. In Fuzzy Logicbased Bit Plane Complexity Segmentation the visual attacks, RS statisticalattack, Sample Pair Analysis is achieved low, because to embed the secret datathe red, green, blue channel were used with fuzzy rules but Difference ImageHistogram is achieved high.
Fig. 6: Detection of EmbeddedData’sVI. CONCLUSIONIn medical information system, medicaldata is easily captured when being storing, receiving or transmission throughcomputer network and Internet. Cryptology and steganography are generally usedto ensure medical data security.
For this reason, this study proposes newalgorithm, Fuzzy Logic-based Bit Plane Complexity Segmentation (FL-BPCS) tosecure medical data. EEG signals and MR images of epilepsy patients are used tocombine multiple medical signals into one file format. The embedding messagesare composed of EEG signals,doctor’s comment, and patient information in file header of DICOM images.
Themessages are secured by using Huffman lossless compression methods and Rijndaelsymmetric algorithm with 128 bit-key to prevent the attacks. The capacity of proposed algorithm ishigher than the result of similar studies in literature. According to theobtained result, the proposed method ensures the confidentiality of thepatient’s information. The FL-BPCS method hides EEG signals, patient’sinformation and doctor’s comment in the pixels of MR images.
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