RST Invariant Blind Image Watermarking Schemes Based on Discrete Tchebichef Transform and Singular Value Decomposition

Senapati, Ranjan K.; Srivastava, S. P.; Mankar, Prasanth

doi:10.1007/s13369-020-04387-9

Cited by 10 publications

(7 citation statements)

References 34 publications

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“…To show the competitiveness of our approach, we compare it with our previous work [ 24 ] as well as schemes in [ 13 , 21 , 26 , 27 , 28 , 29 , 34 , 44 , 45 ] in terms of imperceptibility and robustness.…”

Section: Experimental Resultsmentioning

confidence: 99%

“…Table A3 sketches the comparison of the robustness of the proposed technique with the scheme in [ 44 ] after applying several attacks, such as additive noise, median filtering, histogram equalization, JPEG, rotation, and scaling. It can be observed from Table A3 that the proposed algorithm shows high robustness for Gaussian noise, histogram equalization, rotation, and JPEG (when QF is great than 50) attacks.…”

Section: Experimental Resultsmentioning

confidence: 99%

“…A rotation, scale, and translation invariant watermarking scheme based on discrete Tchebichef transform (DTT), singular value decomposition (SVD), and scale-invariant feature transform (SIFT) is proposed [ 44 ]. The DTT coefficients of the image are arranged similarly to the sub-band scheme generating LL, HL, LH, and HH sub-bands.…”

Section: Previous Workmentioning

confidence: 99%

See 2 more Smart Citations

A Hybrid Robust Image Watermarking Method Based on DWT-DCT and SIFT for Copyright Protection

Hamidi¹,

Haziti

Cherifi³

2021

J. Imaging

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In this paper, a robust hybrid watermarking method based on discrete wavelet transform (DWT), discrete cosine transform (DCT), and scale-invariant feature transformation (SIFT) is proposed. Indeed, it is of prime interest to develop robust feature-based image watermarking schemes to withstand both image processing attacks and geometric distortions while preserving good imperceptibility. To this end, a robust watermark is embedded in the DWT-DCT domain to withstand image processing manipulations, while SIFT is used to protect the watermark from geometric attacks. First, the watermark is embedded in the middle band of the discrete cosine transform (DCT) coefficients of the HL1 band of the discrete wavelet transform (DWT). Then, the SIFT feature points are registered to be used in the extraction process to correct the geometric transformations. Extensive experiments have been conducted to assess the effectiveness of the proposed scheme. The results demonstrate its high robustness against standard image processing attacks and geometric manipulations while preserving a high imperceptibility. Furthermore, it compares favorably with alternative methods.

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Section: Experimental Resultsmentioning

confidence: 99%

Section: Experimental Resultsmentioning

confidence: 99%

See 1 more Smart Citation

A Hybrid Robust Image Watermarking Method Based on DWT-DCT and SIFT for Copyright Protection

Hamidi¹,

Haziti

Cherifi³

2021

J. Imaging

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“…This transformation is an orthonormal tchebichef polynomial, which uses a recursive polynomial 𝑟 𝑝 (𝑥) to transform the image, where 𝑥 defined∈ 0,1, … 𝑁 − 1 [14]. DTT has been widely implemented in watermarking methods, such as in research [15,16] for image and research [1], [17] for video. In implementing it, DTT is mostly done on image blocks, where the image is generally divided into smaller sizes such as 8×8 or 4×4, even 2×2.…”

Section: Discrete Tchebichef Transform (Dtt)mentioning

confidence: 99%

Imperceptible Improvement using Edge Area Selection for Robust Video Watermarking Using Tchebichef - Singular Value Decomposition

2022

IJIES

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Watermarking is a technology to protect the copyright of multimedia data. Video is one of the most complex media compared to image or audio. In invisible watermarking, imperceptible aspects are important besides robustness. This research proposes a frame selection method based on the edge area. The frame with the most edge area is selected and used for watermark embedding with Tchebichef transformation and singular value decomposition. A dual-stage Arnold transform was carried out to spread the edge area, and the watermark was embedded more evenly to improve the imperceptibility quality. Based on the test results, this method can improve the peak signal to noise ratio (PSNR) quality by around 1dB to 2dB while still maintaining the robustness of the watermark compared to the previous method without edge area selection, and dual-stage Arnold transform.

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“…Hong et al apply singular value decomposition (SVD) to image recognition and use the results of SVD to represent the features of images [1]. Then, many scholars have accepted the SVD as an essential tool for analyzing images and applied it to image compression [2], [3], watermarking [4], [5], and low-rank approximation [6], [7], to name a few.…”

Section: A Backgroundsmentioning

confidence: 99%

Approximation of Images via Generalized Higher Order Singular Value Decomposition over Finite-dimensional Commutative Semisimple Algebra

Liao¹,

Shen²,

Li³

et al. 2022

Preprint

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Low-rank approximation of images via singular value decomposition is well-received in the era of big data. However, singular value decomposition (SVD) is only for order-two data, i.e., matrices. It is necessary to flatten a higher order input into a matrix or break it into a series of order-two slices to tackle higher order data such as multispectral images and videos with the SVD. Higher order singular value decomposition (HOSVD) extends the SVD and can approximate higher order data using sums of a few rank-one components. We consider the problem of generalizing HOSVD over a finite dimensional commutative algebra. This algebra, referred to as a t-algebra, generalizes the field of complex numbers. The elements of the algebra, called t-scalars, are fix-sized arrays of complex numbers. One can generalize matrices and tensors over t-scalars and then extend many canonical matrix and tensor algorithms, including HOSVD, to obtain higher-performance versions. The generalization of HOSVD is called THOSVD. Its performance of approximating multi-way data can be further improved by an alternating algorithm. THOSVD also unifies a wide range of principal component analysis algorithms.To exploit the potential of generalized algorithms using tscalars for approximating images, we use a pixel neighborhood strategy to convert each pixel to a "deeper-order" tscalar. Experiments on publicly available images show that the generalized algorithm over t-scalars, namely THOSVD, compares favorably with its canonical counterparts.

show abstract

RST Invariant Blind Image Watermarking Schemes Based on Discrete Tchebichef Transform and Singular Value Decomposition

Cited by 10 publications

References 34 publications

A Hybrid Robust Image Watermarking Method Based on DWT-DCT and SIFT for Copyright Protection

A Hybrid Robust Image Watermarking Method Based on DWT-DCT and SIFT for Copyright Protection

Imperceptible Improvement using Edge Area Selection for Robust Video Watermarking Using Tchebichef - Singular Value Decomposition

Approximation of Images via Generalized Higher Order Singular Value Decomposition over Finite-dimensional Commutative Semisimple Algebra

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