Paper Info
Title
Multi-rate reference embedding for highly-scalable self-recovery using fuzzy mamdani.
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.
Year
DOI
Venue
2019
10.3233/JIFS-181874
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
Keywords
Field
DocType
Multi-rate reference data,highly-scalable self-recovery,fuzzy-based rate allocation,source-channel coding scheme,tamper tolerability
Self recovery,Embedding,Fuzzy logic,Artificial intelligence,Machine learning,Mathematics,Scalability
Journal
Volume
Issue
ISSN
37
5.0
1064-1246
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
Navid Daneshmandpour100.34
Habibollah Danyali24911.07
Mohammad Sadegh Helfroush37011.30