A fuzzy c-means clustering-based fragile watermarking scheme for image authentication

Chen, Wei-Che; Wang, Ming-Shi

doi:10.1016/j.eswa.2007.11.018

Cited by 108 publications

(63 citation statements)

References 16 publications

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“…An authentication data is generated for each block by using a pseudo random sequence seeded with a secret key. In [5] the authors shown that the blockwise independency watermarking scheme can detect tamper on various types of attack, as "cut and paste" and "vector quantization counterfeiting" attacks. The authors show that the tamper detection results are better that those ones obtained with the approaches described in [4,16].…”

Section: Tamper Analysismentioning

confidence: 99%

“…Section 2 describes the image compression method realized with the F-transforms, in Section 3 we recall briefly the FCM method. In Section 4 we describe the Chen and Wang [5] watermarking scheme and in Section 5 we present our fragile watermarking process. Section 6 contains the results of our tests made on a well known image and final considerations are reported in Section 7.…”

Section: Tamper Analysismentioning

confidence: 99%

“…In [11] we showed that the F-transforms give better results with respect to the fuzzy relation equations and give results comparable with the ones obtained by using the JPEG compression method in terms of image quality and coding/decoding time. The watermark insertion is made with the block-based watermarking scheme described in [5]. In this method a fuzzy partition of the blocks is performed by using the Fuzzy C-Means (shortly, FCM) algorithm [2] to realize the blockwise independency and a relation between blocks.…”

Section: Tamper Analysismentioning

confidence: 99%

“…Many tests are performed for both tamper detection and localization processes and for different types of manipulations. The blockwise independency watermarking scheme [5] is applied to the compressed source image; the tampered image is compressed and compared with the coded source marked image. Section 2 describes the image compression method realized with the F-transforms, in Section 3 we recall briefly the FCM method.…”

Section: Tamper Analysismentioning

confidence: 99%

“…Then many fragile watermarking algorithms have been proposed in literature (cfr., for example, [1,3,4,5,9,13,15,16,17,24,28]). The insertion of the watermark, the tamper detection and the tamper localization are the three procedures which compose a fragile watermark and below described.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Fuzzy Transforms and Fragile Watermarking Tamper Detection on Coded Images

Martino

Sessa²

2011

Proceedings of the 7th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT-2011)

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An original image is compressed with fuzzy transforms and is divided in images of sizes 2×2 on which we apply a known fragile watermarking process. A preprocessing phase is considered to determine the better compression rate for the coding process. We test this scheme in tamper detection analysis on a sample of images. The results are good in terms of accuracy for tamper detection with compressed images and in terms of dimension of the image dataset used for storage of the original images.

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Section: Tamper Analysismentioning

confidence: 99%

Section: Tamper Analysismentioning

confidence: 99%

Section: Tamper Analysismentioning

confidence: 99%

Section: Tamper Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Fuzzy Transforms and Fragile Watermarking Tamper Detection on Coded Images

Martino

Sessa²

2011

Proceedings of the 7th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT-2011)

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Intelligent Image Watermarking for Copyright Protection

Roy

Basu²,

Chattopadhyay

2021

Intelligent Multi‐modal Data Processing

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Bayesian inference and joint probability analysis for batch process monitoring

Song

2013

AIChE Journal

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A new probabilistic monitoring method for batch processes that have multiple operating conditions is described. Particularly, for multiphase batch processes, a phase-based Bayesian inference strategy is introduced, which can efficiently combine the information of multiple operation modes together into a single model in each specific phase. Therefore, without any process knowledge, local monitoring results in different operation modes can be automatically integrated. Besides, the information of the operation mode can be obtained through joint probability analysis under the Bayesian monitoring framework. Potential extensions of the proposed method for fault diagnosis and identification are also discussed. A benchmark case study on the penicillin fermentation process is given to evaluate the feasibility and efficiency of the proposed method. It is demonstrated that the monitoring performance and the process comprehension have both been improved.

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A fuzzy c-means clustering-based fragile watermarking scheme for image authentication

Cited by 108 publications

References 16 publications

Fuzzy Transforms and Fragile Watermarking Tamper Detection on Coded Images

Fuzzy Transforms and Fragile Watermarking Tamper Detection on Coded Images

Intelligent Image Watermarking for Copyright Protection

Bayesian inference and joint probability analysis for batch process monitoring

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