scholarly journals SVD-Based Image Watermarking Using the Fast Walsh-Hadamard Transform, Key Mapping, and Coefficient Ordering for Ownership Protection

Symmetry ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 52 ◽  
Author(s):  
Tahmina Khanam ◽  
Pranab Kumar Dhar ◽  
Saki Kowsar ◽  
Jong-Myon Kim
Keyword(s):  
State Of The Art ◽  
Image Watermarking ◽  
Singular Values ◽  
Multimedia Data ◽  
Cover Image ◽  
Hadamard Transform ◽  
Art Methods ◽  
Walsh Hadamard Transform ◽  
Value Decomposition ◽  
Ownership Protection

Proof of ownership on multimedia data exposes users to significant threats due to a myriad of transmission channel attacks over distributed computing infrastructures. In order to address this problem, in this paper, an efficient blind symmetric image watermarking method using singular value decomposition (SVD) and the fast Walsh-Hadamard transform (FWHT) is proposed for ownership protection. Initially, Gaussian mapping is used to scramble the watermark image and secure the system against unauthorized detection. Then, FWHT with coefficient ordering is applied to the cover image. To make the embedding process robust and secure against severe attacks, two unique keys are generated from the singular values of the FWHT blocks of the cover image, which are kept by the owner only. Finally, the generated keys are used to extract the watermark and verify the ownership. The simulation result demonstrates that our proposed scheme is highly robust against numerous attacks. Furthermore, comparative analysis corroborates its superiority among other state-of-the-art methods. The NC of the proposed method is numerically one, and the PSNR resides from 49.78 to 52.64. In contrast, the NC of the state-of-the-art methods varies from 0.7991 to 0.9999, while the PSNR exists in the range between 39.4428 and 54.2599.

A SVD and Modified Firefly Optimization Based Robust Digital Image Watermarking Technique

2017 ◽  
Vol 7 (7) ◽  
pp. 268
Author(s):  
Chauhan Usha ◽  
Singh Rajeev Kumar
Keyword(s):  
Digital Media ◽  
Firefly Algorithm ◽  
Image Watermarking ◽  
Singular Values ◽  
Robust Watermarking ◽  
Scaling Factors ◽  
Geometric Attacks ◽  
Host Image ◽  
Firefly Optimization ◽  
Value Decomposition

Digital Watermarking is a technology, to facilitate the authentication, copyright protection and Security of digital media. The objective of developing a robust watermarking technique is to incorporate the maximum possible robustness without compromising with the transparency. Singular Value Decomposition (SVD) using Firefly Algorithm provides this objective of an optimal robust watermarking technique. Multiple scaling factors are used to embed the watermark image into the host by multiplying these scaling factors with the Singular Values (SV) of the host image. Firefly Algorithm is used to optimize the modified host image to achieve the highest possible robustness and transparency. This approach can significantly increase the quality of watermarked image and provide more robustness to the embedded watermark against various attacks such as noise, geometric attacks, filtering attacks etc.

LWT-DCT based Image Watermarking Scheme using Normalized SVD

2020 ◽  
Vol 13 ◽  
Author(s):  
Rahul Dixit ◽  
Amita Nandal ◽  
Arvind Dhaka ◽  
Vardan Agarwal ◽  
Yohan Varghese
Keyword(s):  
Wavelet Transform ◽  
Singular Value Decomposition ◽  
Discrete Cosine Transform ◽  
Image Watermarking ◽  
Singular Value ◽  
Cover Image ◽  
Lifting Wavelet Transform ◽  
Cosine Transform ◽  
Lifting Wavelet ◽  
Value Decomposition

Background: Nowadays information security is one of the biggest issues of social networks. The multimedia data can be tampered with, and the attackers can then claim its ownership. Image watermarking is a technique that is used for copyright protection and authentication of multimedia. Objective: We aim to create a new and more robust image watermarking technique to prevent illegal copying, editing and distribution of media. Method : The watermarking technique proposed in this paper is non-blind and employs Lifting Wavelet Transform on the cover image to decompose the image into four coefficient matrices. Then Discrete Cosine Transform is applied which separates a selected coefficient matrix into different frequencies and later Singular Value Decomposition is applied. Singular Value Decomposition is also applied to the watermarking image and it is added to the singular matrix of the cover image which is then normalized followed by the inverse Singular Value Decomposition, inverse Discrete Cosine Transform and inverse Lifting Wavelet Transform respectively to obtain an embedded image. Normalization is proposed as an alternative to the traditional scaling factor. Results: Our technique is tested against attacks like rotation, resizing, cropping, noise addition and filtering. The performance comparison is evaluated based on Peak Signal to Noise Ratio, Structural Similarity Index Measure, and Normalized Cross-Correlation. Conclusion: The experimental results prove that the proposed method performs better than other state-of-the-art techniques and can be used to protect multimedia ownership.

scholarly journals Pengamanan Hak Cipta Citra Digital dengan Teknik Watermarking Menggunakan Metode Hybrid SVD dengan DWT

2021 ◽  
Vol 2 (11) ◽  
pp. 2145-2157
Author(s):  
Ondi Asroni ◽  
Dedy Ricardo Serumena
Keyword(s):  
Singular Value Decomposition ◽  
Digital Watermarking ◽  
Image Watermarking ◽  
Singular Value ◽  
Cover Image ◽  
Visible Watermark ◽  
Value Decomposition

Era zaman digital saat ini, internet telah menjadi sebuah kebutuhan sehari-hari, yang memberikan kemudahan terhadap pengguna untuk melakukan aktivitas transmisi file, namun demikian data tersebut membutuhkan proteksi dari tangan yang tidak bertanggung jawab. Dalam penelitian ini di ajukan sebuah metode digital watermarking dalam melakukan perlindungan terhadap motif batik. Adapun algoritma yang diterapkan yaitu Hybrid Singular Value Decomposition dengan Discrete Wavelete Transform, setelah dilakukan experimen penerapan Hybrid Image Watermarking DWT dengan SVD nilai alpha dapat mempengaruhi tingkat imperceptibility terhadap citra watermarked, karena nilai alpha menunjukkan tingkat ketampakan (visible) watermark pada cover image. Semakin rendah nilai alpha maka tingkat ketampakan watermark semakin berkurang dan tingkat imperceptibility semakin tinggi. Dari pengujian yang dilakukan dengan object motif batik ditemukan nilai alpha terbaik yaitu nilai alpha 0.01, karena mendapatkan nilai PSNR tertinggi dari yang lainnya, sedangkan Berdasarkan pengujian tingkat robustness metode Hybrid Image Watermarking DWT-SVD pada subband LL memiliki tingkat ketahanan yang cukup tinggi terhadap upaya menghilangkan watermark yang menjadi identitas kepemilikan sah terhadap suatu citra digital selama citra watermarked tidak direkayasa dengan serangan noise.

ADAPTIVE IMAGE WATERMARKING USING DISCRETE WAVELET TRANSFORM, SINGULAR VALUE DECOMPOSITION AND AUTOMORPHISM

2018 ◽  
Vol 6 (2) ◽  
pp. 77-80
Author(s):  
Om Narayan Mishra ◽  
Shailja Shukla
Keyword(s):  
Wavelet Transform ◽  
Singular Value Decomposition ◽  
Discrete Wavelet Transform ◽  
Image Watermarking ◽  
Singular Value ◽  
Cover Image ◽  
Discrete Wavelet ◽  
Object Image ◽  
Xor Operation ◽  
Value Decomposition

Watermarking is a method to hide the image efficiently into any covering object (image in our case) so no intruder can interpret it by any means. Proposed work is a new design of image watermarking which include pre-processing of cover image with Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD). Proposed work using an alphanumeric key which initially modifies the watermark using simple ‘XOR’ operation, and at the receiver end this key must be there so that receiver can extract the watermark.  Proposed work is also using torus Automorphism which initially changes the watermark into a scramble format which cannot be recognised as original watermark.

scholarly journals A new DWT-SVD based robust watermarking scheme for real property rights

2014 ◽  
Vol 3 (2) ◽  
pp. 69-78
Author(s):  
Sedigeh Razavi babakalak ◽  
Mohammad Ali Balafar ◽  
Ali Farzan
Keyword(s):  
Property Rights ◽  
Image Watermarking ◽  
Singular Values ◽  
Discrete Wavelet ◽  
Digital Image Watermarking ◽  
Robust Watermarking ◽  
Real Property ◽  
Noise Type ◽  
Value Decomposition ◽  
Watermarking Scheme

In this paper, a new robust digital image watermarking algorithm which was based on singular value decomposition (SVD) and discrete wavelet transform (DWT) was proposed and simulated for protecting real property rights. A gray scale logo image, rather than a randomly generated Gaussian noise type watermark, was used as a watermark. Its embedding algorithm hid a watermark LL sub-band blocks in the low–low (LL) and high-high (HH) sub-bands of a target non-overlapping block of the host image by modifying singular values on SVD version of these blocks. A semi-blind watermark extraction was designed to estimate the original coefficients. Experimental results showed that the proposed scheme made significant improvements in terms of both transparency and robustness and was superior to the existing methods which were considered in this paper.

scholarly journals Blind Audio Watermarking Based on Parametric Slant-Hadamard Transform and Hessenberg Decomposition

Symmetry ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 333
Author(s):  
Pranab Kumar Dhar ◽  
Azizul Hakim Chowdhury ◽  
Takeshi Koshiba
Keyword(s):  
State Of The Art ◽  
Audio Watermarking ◽  
Hadamard Transform ◽  
Watermark Image ◽  
Hessenberg Matrix ◽  
Multimedia Contents ◽  
Simulation Results ◽  
Ownership Protection ◽  
Host Signal ◽  
Square Matrix

Digital watermarking has been widely utilized for ownership protection of multimedia contents. This paper introduces a blind symmetric audio watermarking algorithm based on parametric Slant-Hadamard transform (PSHT) and Hessenberg decomposition (HD). In our proposed algorithm, at first watermark image is preprocessed to enhance the security. Then, host signal is divided into non-overlapping frames and the samples of each frame are reshaped into a square matrix. Next, PSHT is performed on each square matrix individually and a part of this transformed matrix of size m×m is selected and HD is applied to it. Euclidean normalization is calculated from the 1st column of the Hessenberg matrix, which is further used for embedding and extracting the watermark. Simulation results ensure the imperceptibility of the proposed method for watermarked audios. Moreover, it is demonstrated that the proposed algorithm is highly robust against numerous attacks. Furthermore, comparative analysis substantiates its superiority among other state-of-the-art methods.

scholarly journals Matrix decompositions using sub-Gaussian random matrices

2018 ◽  
Vol 8 (3) ◽  
pp. 445-469 ◽  
Author(s):  
Yariv Aizenbud ◽  
Amir Averbuch
Keyword(s):  
Error Bound ◽  
State Of The Art ◽  
Matrix Decomposition ◽  
Singular Values ◽  
Random Projection ◽  
Error Rates ◽  
The State ◽  
Matrix Decompositions ◽  
Asymptotic Complexity ◽  
Value Decomposition

Abstract In recent years, several algorithms which approximate matrix decomposition have been developed. These algorithms are based on metric conservation features for linear spaces of random projection types. We present a new algorithm, which achieves with high probability a rank-$r$ singular value decomposition (SVD) approximation of an $n \times n$ matrix and derive an error bound that does not depend on the first $r$ singular values. Although the algorithm has an asymptotic complexity similar to state-of-the-art algorithms and the proven error bound is not as tight as the state-of-the-art bound, experiments show that the proposed algorithm is faster in practice while providing the same error rates as those of the state-of-the-art algorithms. We also show that an i.i.d. sub-Gaussian matrix with large probability of having null entries is metric conserving. This result is used in the SVD approximation algorithm, as well as to improve the performance of a previously proposed approximated LU decomposition algorithm.

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