1st International ICST Workshop on Data Hiding for Information and Multimedia Security

Research Article

Structural Digital Signature and Semi-Fragile Fingerprinting for Image Authentication in Wavelet Domain

  • @INPROCEEDINGS{10.1109/IAS.2007.46,
        author={Yan  Zhu and Chang-Tsun Li and Hong-Jia  Zhao},
        title={Structural Digital Signature and Semi-Fragile Fingerprinting for Image Authentication in Wavelet Domain},
        proceedings={1st International  ICST Workshop on Data Hiding for Information and Multimedia Security},
        publisher={IEEE},
        proceedings_a={DHIM},
        year={2007},
        month={9},
        keywords={Authentication  Computer science  Data mining  Digital signatures  Fingerprint recognition  Image coding  Information security  Quantization  Watermarking  Wavelet domain},
        doi={10.1109/IAS.2007.46}
    }
    
  • Yan Zhu
    Chang-Tsun Li
    Hong-Jia Zhao
    Year: 2007
    Structural Digital Signature and Semi-Fragile Fingerprinting for Image Authentication in Wavelet Domain
    DHIM
    IEEE
    DOI: 10.1109/IAS.2007.46
Yan Zhu1,*, Chang-Tsun Li2,*, Hong-Jia Zhao1
  • 1: Institute of Computer Science and Technology Peking University, 100871, Beijing, China
  • 2: Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK
*Contact email: zhuyan@icst.pku.edu.cn, ctli@dcs.warwick.ac.uk

Abstract

In this paper, a novel semi-fragile authentication scheme based on block-mean quantization in the wavelet domain is proposed for image authentication. The scheme is composed of a structural digital signature process and a semi-fragile watermarking/fingerprinting algorithm. In the signature process, the invariants are extracted as authentication codes from the quantization relationships and hierarchy information of discrete wavelet decomposition. The authentication codes consist of inter-block and intra-block codes in order to detect and localize tampering. The semi-fragile fingerprinting algorithm is intended to embed the signature via the use of block-mean quantization. The proposed scheme can effectively detect and localize the malicious modification while tolerating lossy compression.