Secure Medical Image Steganography Method Based on Pixels Variance Value and Eight Neighbors

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dc.contributor.authorAbed, Mohammed K.
dc.contributor.authorKareem, Marwah M.
dc.contributor.authorIbrahim, Raed Khalid
dc.contributor.authorHashim, Mohammed M.
dc.contributor.authorKurnaz, Sefer
dc.contributor.authorAli, Adnan Hussein
dc.date.accessioned2025-02-06T18:01:20Z
dc.date.available2025-02-06T18:01:20Z
dc.date.issued2021
dc.departmentAltınbaş Üniversitesien_US
dc.description2021 International Conference on Advanced Computer Applications, ACA 2021 -- 25 July 2021 through 26 July 2021 -- Maysan -- 175223en_US
dc.description.abstractThe security aspect of processes and methodologies in the information and communication technology era is the main part. The security of information should a key priority in the secret exchange of information between two parties. In order to ensure the security of information, there are some strategies that are used, and they include steganography, watermark, and cryptography. In cryptography, the secrete message is converted into unintelligible text, but the existence of the secrete message is noticed, on the other hand, watermarking and steganography involve hiding the secrete message in a way that its presence cannot be noticed. Presently, the design and development of an effective image steganography system are facing several challenges such as the low capacity, poor robustness and imperceptibility. To surmount these challenges, a new secure image steganography work called the Pixels Variance (PV) method is proposed along with the eight neighbors method and Huffman coding algorithm to overcome the imperceptibility and capacity issues. In proposed method, a new image partitioning with Henon map is used to increase the security part and has three main stages (preprocessing, embedding, and extracting) each stage has different process. In this method, different standard images were used such as medical images and SIPI-dataset. The experimental result was evaluated with different measurement parameters such as Peak signal-to-noise ratio (PSNR) and Structural Similarity Index (SSIM). In short, the proposed steganography method outperformed the commercially available data hiding schemes, thereby resolved the existing issues. © 2021 IEEE.en_US
dc.identifier.doi10.1109/ACA52198.2021.9626807
dc.identifier.endpage205en_US
dc.identifier.isbn978-166543503-1
dc.identifier.scopus2-s2.0-85123471732
dc.identifier.startpage199en_US
dc.identifier.urihttps://doi.org/10.1109/ACA52198.2021.9626807
dc.identifier.urihttps://hdl.handle.net/20.500.12939/5326
dc.indekslendigikaynakScopus
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof2021 International Conference on Advanced Computer Applications, ACA 2021en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmzKA_Scopus_20250206
dc.subjectCompression methoden_US
dc.subjectEight neighborsen_US
dc.subjectImage steganographyen_US
dc.subjectimperceptibilityen_US
dc.subjectSecurityen_US
dc.titleSecure Medical Image Steganography Method Based on Pixels Variance Value and Eight Neighborsen_US
dc.typeConference Objecten_US

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