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Cited by 531

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Digital watermarking for telltale tamper proofing and authentication

Kundur, D., Hatzinakos, D.·1999·Proceedings of the IEEE

Abstract

In this paper, we consider the problem of digital watermarking to ensure the credibility of multimedia. We specifically address the problem of fragile digital watermarking for the tamper proofing of still images. Applications of our problem include authentication for courtroom evidence, insurance claims, and journalistic photography. We present a novel fragile watermarking approach which embeds a watermark in the discrete wavelet domain of the image by quantizing the corresponding coefficients. Tamper detection is possible in localized spatial and frequency regions. Unlike previously proposed techniques, this novel approach provides information on specific frequencies of the image that have been modified. This allows the user to make application-dependent decisions concerning whether an image, which is JPEG compressed for instance, still has credibility. Analysis is provided to evaluate the performance of the technique to varying system parameters. In addition, we compare the performance of the proposed method to existing fragile watermarking techniques to demonstrate the success and potential of the method for practical multimedia tamper proofing and authentication.

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Metadata

Authors
Kundur, D., & Hatzinakos, D.
Venue
Proceedings of the IEEE
Published
1999-07
Volume
87
Issue
7
Pages
1167-1180
ISSN
0018-9219 (ISSN-L)
OpenAlex
W2166820261
Cited by
531
References
1
FWCI
20.96
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Digital watermarking for telltale tamper proofing and authentication. https://app.cochat.ai/papers/10.1109/5.771070
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Kundur, D., & Hatzinakos, D. (1999). Digital watermarking for telltale tamper proofing and authentication. Proceedings of the IEEE. https://doi.org/10.1109/5.771070

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.