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Article type: Research Article
Authors: Daneshmandpour, Navid | Danyali, Habibollah; * | Helfroush, Mohammad Sadegh
Affiliations: Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, Iran
Correspondence: [*] Corresponding author. Habibollah Danyali, Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, Iran. E-mail: danyali@sutech.ac.ir.
Abstract: This paper proposes a self-recovery method of fragile watermarking. Generally, self-recovery methods embed two types of data into the original image: check-bits for tamper detection and reference data for image recovery. Generating reference data is the primary challenge of every self-recovery method for more tamper resiliency and higher reconstruction quality. The proposed Multi-Rate Reference Embedding (MRRE) method makes unique reference data with several redundancy rates, instead of generating multiple reference data. According to the proposed methodology, the image is compressed by a source coding algorithm and the compressed data is separated into ten parts. Each part is protected by a channel coding algorithm based on pre-assigned redundancy rates. A fuzzy-based rate allocation system is used to assign the redundancy rates based on the importance of data. The generated data is packetized and randomly embedded into an image block. For tamper detection purpose, check-bits are generated by an MD5 hash function for every block. Both reference data and check-bits are embedded into three least significant bits (LSB) of the image pixels. To increase restoration efficiency, the proposed MRRE method provides ten scales of image recovery named highly-scalable self-recovery. The simulation results show an improvement in both tamper tolerability and reconstruction quality in comparison with the most recent methods.
Keywords: Multi-rate reference data, highly-scalable self-recovery, fuzzy-based rate allocation, source-channel coding scheme, tamper tolerability
DOI: 10.3233/JIFS-181874
Journal: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6471-6481, 2019
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