Targeted watermark removal of a SVD-based image watermarking scheme

Nikbakht, Pegah; Mahdavi, Mojtaba

doi:10.1109/ikt.2015.7288669

Cited by 10 publications

(4 citation statements)

References 12 publications

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“…DWT Sub-images [10] In Figure 1, the sub-image in HL, LH, and HH positions will look like a rough version of the host image because it contains high-frequency components of the host image, while for the LL sub-image looks like the host image and looks smoother as it contains low-frequency components of the host image [14]. Since it is similar to the host image, the LL sub-image can embed the watermark image to the host image [6]. While the pixel values of LH, HL, and HH sub-images tend to be low-value and sometimes zero, so they are easy to compress.…”

Section: D-discrete Wavelet Transformmentioning

confidence: 99%

“…The visible watermark [6] is good to know directly who the owner of the artworks but in that way, it will degrade the quality of work because it will be covered by the watermark and make irresponsible people to easily remove the watermark in various ways, whereas if watermarking is done invisibly [4] the work will not be covered and confuse irresponsible people who want to remove the watermark from the artwork.…”

Section: Introductionmentioning

confidence: 99%

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DWT-SVD Combination Method for Copyrights Protection

Rachmawanto

Sari

Pradana

2020

SJI

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Many irresponsible people who download other people's artwork then modify and redistribute the work of someone without permission by not including the owner's name. Based on that problem an attempt was made to provide an invisible watermark so as not to be easy to do the watermark removal process on the artwork, using a 2-level DWT and SVD combination method that can embed watermarks invisibly and increase watermarking resistance to the image from various image manipulation processes such as noise, JPEG compression, and so on. From combination method of DWT-SVD got the highest PSNR value with watermark image size 32x32 and 64x64 which is 59.2656 dB and 54.7414 dB, respectively.

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Section: D-discrete Wavelet Transformmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

DWT-SVD Combination Method for Copyrights Protection

Rachmawanto

Sari

Pradana

2020

SJI

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“…In other words, if the algorithm is known, the watermark can be irreversibly damaged through targeted attacks. Examples of such attacks are presented in [2,3,4,5,6]. In the following, the algorithms are briefly presented and the extracted features of each i.e.…”

Section: Detecting Watermarking Algorithm Usingmentioning

confidence: 99%

“…A number of targeted attacks on watermarking algorithms are presented in [2,3,4,5,6], all of which require prior knowledge of the watermarking algorithm. This prerequisite may appear to prevent some targeted attacks.…”

Section: Introductionmentioning

confidence: 99%

Feature extraction for detection of watermarking algorithm

Hatefi

Mahdavi

Nikbakht

2016

2016 13th International Iranian Society of Cryptology Conference on Information Security and Cryptology (ISCISC)

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The concept of digital watermarking has been developed to solve problems such as illegal duplication and distribution of digital media. In watermarking, the process of removing the watermark from the media is known as an attack. Typically, attacks are carried out using tools such as Stirmark. Some attacks are executed in a targeted manner; in other words, knowing the watermarking algorithm, they directly seek to destroy the watermark in the media. In this type of attack, the damage caused to the media is less extensive than generalized attacks such as Stirmark. Clearly, targeted attacks require prior knowledge about the watermarking algorithm. To the best of our knowledge, algorithm detection in watermarking remains to be investigated. One possible approach is to use staganalysis feature sets; however, we demonstrate that, despite their large number of features, such feature sets do not produce adequate results for watermarking. In this paper, several features are introduced, which can be used in an SVM classifier to allow the detection of the watermarking algorithm. According to implementation results, although the proposed feature set is small, its accuracy is substantially greater than that of the staganalysis feature sets.

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