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Watermark-Only Security Attack on DM-QIM Watermarking: Vulnerability to Guided Key Guessing

Watermark-Only Security Attack on DM-QIM Watermarking: Vulnerability to Guided Key Guessing

B. R. Matam, David Lowe
Copyright: © 2010 |Volume: 2 |Issue: 2 |Pages: 24
ISSN: 1941-6210|EISSN: 1941-6229|EISBN13: 9781609604042|DOI: 10.4018/jdcf.2010040105
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MLA

Matam, B. R., and David Lowe. "Watermark-Only Security Attack on DM-QIM Watermarking: Vulnerability to Guided Key Guessing." IJDCF vol.2, no.2 2010: pp.64-87. http://doi.org/10.4018/jdcf.2010040105

APA

Matam, B. R. & Lowe, D. (2010). Watermark-Only Security Attack on DM-QIM Watermarking: Vulnerability to Guided Key Guessing. International Journal of Digital Crime and Forensics (IJDCF), 2(2), 64-87. http://doi.org/10.4018/jdcf.2010040105

Chicago

Matam, B. R., and David Lowe. "Watermark-Only Security Attack on DM-QIM Watermarking: Vulnerability to Guided Key Guessing," International Journal of Digital Crime and Forensics (IJDCF) 2, no.2: 64-87. http://doi.org/10.4018/jdcf.2010040105

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Abstract

This paper addresses the security of a specific class of common watermarking methods based on Dither modulation-quantisation index modulation (DM-QIM) and focusing on watermark-only attacks (WOA). The vulnerabilities of and probable attacks on lattice structure based watermark embedding methods have been presented in the literature. DM-QIM is one of the best known lattice structure based watermarking techniques. In this paper, the authors discuss a watermark-only attack scenario (the attacker has access to a single watermarked content only). In the literature it is an assumption that DM-QIM methods are secure to WOA. However, the authors show that the DM-QIM based embedding method is vulnerable against a guided key guessing attack by exploiting subtle statistical regularities in the feature space embeddings for time series and images. Using a distribution-free algorithm, this paper presents an analysis of the attack and numerical results for multiple examples of image and time series data.

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