SVM-based robust image watermarking technique in LWT domain using different sub-bands

Islam, Mohiul; Roy, Amarjit; Laskar, Rabul Hussain

doi:10.1007/s00521-018-3647-2

Cited by 56 publications

(58 citation statements)

References 46 publications

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“…The red boundary in the given figure consists of the steps involved in stage one, i.e. the binary watermark embedding in greyscale and it is inspired by the literature in [11–13 ]. The green box depicts the stage two of the process that generates the final watermarked multitone image.…”

Section: Methodsmentioning

confidence: 99%

“…Based on the aforementioned reasons,

L H 3

subband (represented by solid yellow) is selected for watermark embedding in this Letter. A more detailed analysis of watermark embedding in each of these subbands can be found in [13 ]. The rest of the proposed watermarking strategy is covered within the upcoming section on watermark embedding in this Letter.…”

Section: Methodsmentioning

confidence: 99%

“…[12 ] for more details on this shuffling concept). Furthermore, as per [12, 13 ], the consecutive non‐overlapping coefficients of

L H 3

subband are grouped into 1024 blocks of size

2 \times 2

each. A secret key 2 is then utilised to randomly shuffle and select 512 out of the total 1024 blocks to embed the binary watermark (W) of size

16 \times 32

(512 bits), respectively.…”

Section: Watermark Embeddingmentioning

confidence: 99%

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Significant difference-based watermarking in multitoned images

Sharma¹,

Zou²,

Fang³

2020

Electronics Letters

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Digital multitoning is an advanced form of halftoning‐based printing technique which renders multiple tones at each pixel of an image. This superior scheme delivers an improved print quality in terms of smooth texture, homogeneity and the suppression of artefacts. This Letter examines the potential scope of multitoning as an effective strategy to embed a binary watermark within a multitoned image. The proposed method uses the significant difference among the highpass wavelet coefficients achieved by discrete wavelet transform for watermark embedding. Furthermore, an improvised ordered dithering technique using adaptive threshold quantisation is also incorporated, which not only makes the proposed method time‐efficient but also offers uniformity to the final image. The proposed watermarking technique is imperceptible and yields watermarked images with high peak signal‐to‐noise ratio value. Experimental simulations demonstrate that the proposed watermarking method is superior to the state‐of‐the‐art methods in the context of both objective and subjective assessments.

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Section: Methodsmentioning

confidence: 99%

“…Based on the aforementioned reasons,

L H 3

Section: Methodsmentioning

confidence: 99%

“…[12 ] for more details on this shuffling concept). Furthermore, as per [12, 13 ], the consecutive non‐overlapping coefficients of

L H 3

subband are grouped into 1024 blocks of size

2 \times 2

each. A secret key 2 is then utilised to randomly shuffle and select 512 out of the total 1024 blocks to embed the binary watermark (W) of size

16 \times 32

(512 bits), respectively.…”

Section: Watermark Embeddingmentioning

confidence: 99%

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Significant difference-based watermarking in multitoned images

Sharma¹,

Zou²,

Fang³

2020

Electronics Letters

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show abstract

“…Because watermark detection in audio files is easier than image files, therefore, audio watermarking is more challenging than other areas of watermarking. Since it has been proven that the human hearing system has more sensitivity than the human vision system [2], (several valid tests have been carried out on the auditory nerve [3]), therefore protection of imperceptibility in audio watermarking is more difficult than image watermarking (like references [4,5]). Because of the high capability of audio in watermarking data, many researchers have focused on this topic today.…”

Section: Introductionmentioning

confidence: 99%

A novel audio watermarking scheme using ensemble-based watermark detector and discrete wavelet transform

Pourhashemi

Mosleh

Erfani

2020

Neural Comput & Applic

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Most existing extraction techniques in audio watermarking use conventional techniques in which some sets of special rules based on reverse embedding rules are used for watermark extraction and have many weaknesses, like very low robustness to destructive attacks. To overcome this problem, the use of machine learning-based methods has increased in recent years in this field. The disadvantage of these methods is the high reliance on a unique classifier and lack of proper efficiency when achieving high capacity, which is a major challenge in audio watermarking. The main purpose of this paper is to present a method that covers the weak points of conventional methods and simple intelligent methods and improves system performance using a synergistic combination of discrete wavelet transform (DWT) and ensemble-intelligent extraction approach by proposed combination of trained machine learning classifiers. For the embedding operation in the proposed method, the DWT and the difference in energy levels obtained through DWT coefficients are used. In the extraction section, three methods are used in parallel: (a) the trained support vector machine (SVM) classifier with RBF kernel, (b) trained SVM classifier with quadratic kernel and (c) the trained K-nearest neighbor classifier; finally, the majority function is used to vote and make a final decision to create an intelligent-based watermark detector. A training set is required to train the classifiers, whose bit sequence is generated by a proposed 5-bit linear-feedback shift register. The results of various experiments indicate that this ensemble method has achieved the appropriate imperceptibility and high capacity, along with higher robustness compared to conventional techniques and individual learning classifiers.

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“…The indicated method revealed significant robustness above signal processing attacks. Islam et al [16], embedded the watermark in a third level Lifting Wavelet Transform (LWT), selected sub-band, and this last, was blended then divided into quadrants and randomized, where different secret keys are used for both operations, in the other part a Support Vector Machine (SVM) was utilized for watermark detection. Here, results are sufficient in terms of robustness and imperceptibility.…”

Section: Introductionmentioning

confidence: 99%

Blind Digital Watermarking Framework Based on DTCWT and NSCT for Telemedicine Application

Nouioua¹,

Seddiki²,

Ghaz³

2020

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In this work, a blind watermarking framework for medical images based on Dual-Tree Complex Wavelet Transform (DTCWT) and Non-subsampled Contourlet Transform (NSCT) is proposed. The core idea of this technique is to embed the watermark in the appropriate NSCT sub-band obtained by decomposing the cover image low coefficients purchased from the DTCWT standing on a quantization embedding function, the extraction phase is done without the requirement of the original cover image, what makes it a fully blind process. As clarity and integrity of the retrieved watermark are mandatory, a series of tests were exerted to affirm the robustness of the proposed scheme. The effectiveness of the watermarking is validated by using the peak signal-to-noise ratio (PSNR) and the structural similarity index measure (SSIM) through experiments. Simulation results demonstrate the invisibility of the proposed method and its strong robustness against various attacks, including additive noise, image filtering, JPEG compression, amplitude scaling, rotation attack, and combinational attack. Furthermore, the method in hands outperformance within the quantitative comparisons with other techniques in the literature in terms of rapid execution time, and quality extraction of hidden information, and appropriateness to be integrated for secure exchange in the healthcare sector.

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SVM-based robust image watermarking technique in LWT domain using different sub-bands

Cited by 56 publications

References 46 publications

Significant difference-based watermarking in multitoned images

Significant difference-based watermarking in multitoned images

A novel audio watermarking scheme using ensemble-based watermark detector and discrete wavelet transform

Blind Digital Watermarking Framework Based on DTCWT and NSCT for Telemedicine Application

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