2020
DOI: 10.1007/s11042-020-09943-x
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Entropy based spatial domain image watermarking and its performance analysis

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Cited by 29 publications
(10 citation statements)
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References 26 publications
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“…The spatial domain-based watermarking algorithms directly modify the pixels of the host image to embed the watermark. One classical algorithm is the least significant bit algorithm (LSB) [8,22], which is simple, but it is less robust to most attacks. The frequency domainbased watermarking algorithms embed the watermark by modifying the frequency domain coefficients of the host image.…”
Section: Embedding-based Watermarking Algorithmsmentioning
confidence: 99%
“…The spatial domain-based watermarking algorithms directly modify the pixels of the host image to embed the watermark. One classical algorithm is the least significant bit algorithm (LSB) [8,22], which is simple, but it is less robust to most attacks. The frequency domainbased watermarking algorithms embed the watermark by modifying the frequency domain coefficients of the host image.…”
Section: Embedding-based Watermarking Algorithmsmentioning
confidence: 99%
“…Digital watermarking, i.e. the process of insertion and extraction of the watermark, can be carried out either in spatial [9][10] or in frequency / transform domain [11][12][13], although some hybrid logics [14][15] have also been developed to get a better result.…”
Section: Classifications Of Digital Watermarkingmentioning
confidence: 99%
“…This information hiding scheme is applicable for all types of multimedia objects, and hence by the type of the cover object, watermarking can be classified as text [16], image [9][10][11][12][13][14], audio [17], and video [15]. As per the scope of this paper, image watermarking is the prime concern.…”
Section: Classifications Of Digital Watermarkingmentioning
confidence: 99%
“…A new approach uses entropy measures to detect key player sets within a social network, providing a simple solution to the KPP-Pos problem [30]. Entropy-based measures are employed to identify the key player sets, but the shortcoming of such methods is that they are limited to nondense heterogeneous networks [31][32][33].…”
Section: B Leverage Analysis -Influence and Disruptionmentioning
confidence: 99%