DWT DCT based new image watermarking algorithm to improve the imperceptibility and robustness

Veni, M.; Meyyappan, T.

doi:10.1109/iceice.2017.8192444

Cited by 7 publications

(5 citation statements)

References 2 publications

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“…Numerous digital image watermarking methods had been suggested in the literature, spatial domain techniques [1][2][3], frequency domain techniques [4][5][6][7][8][9][10], combination with mathematical models [8][9][10][11][12][13] and recent works combined with machine learning [14][15][16][17][18][19] models. Visible watermarking [1] is the first spatial domain watermarking method that work on the pixel value of the image.…”

Section: Related Workmentioning

confidence: 99%

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Machine Learning Based Digital Image Watermarking

2020

jcr

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Watermarking is the method of adding information in digital media for content protection and authentication. Digital image watermarking is one of the solutions to offer value added security on the top of authentication and data encryption for content protection in digital images. The watermarking methods can be frequential and spatial domain methods. In this work, the frequency domain based watermarking method is implemented with the use of discrete wavelet transform (DWT). Along with the frequency domain technique, here we are utilizing the combination of topical developments of the mathematical techniques and most advanced algorithms of machine learning for digital image watermarking process. The mathematical technique considered here is the principal component analysis (PCA) because of its property of dimensionality reduction which further enhances the robustness to watermarking process. The machine learning algorithm taken into account in this work is the support vector machine (SVM) algorithm to increase the accuracy of watermarking process.

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Section: Related Workmentioning

confidence: 99%

“…Abolfazi [11] proposed a new method for color image watermarking based on the DCT-PCA. Veni [12] proposed a new image watermarking algorithm based on combination of DWT-DCT to improve the imperceptibility and robustness. Maniekansai [13] proposed the image watermarking algorithm based on DWT-DCT-SVD for robust watermarking.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning Based Digital Image Watermarking

2020

jcr

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“…The model and embedding method can achieve a payload of more than 200 bps. The technique in [1], a blind digital watermarking algorithm is provided using DWT and DCT. HL coefficient is puzzled first before embedding the scrambled image into a HL coefficient of the cover image.…”

Section: Related Workmentioning

confidence: 99%

User Interface Based High Payload Double Image Steganography

Sankpal¹,

Mr²

2018

International Journal of Advanced Research in Computer and Comm

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Digital data, since its invasion has become an integral part of everyday life aspects. Digital data can be transmitted in a fast and inexpensive way through data communication networks without losing quality. Digital data can be stored efficiently with a very high quality, and it can be manipulated very easily using computers. Watermarking techniques have been developed to protect the digital data from such manipulations. In watermarking, the most challenging task is the embedding capacity in an image. The strength of watermarking can be changed for the improvement of resolution of the images in order to make the image indistinguishable from the original images. The proposed method suggests an effective and easily implementable way to secure the confidential information with a high payload capacity. Also a User Interface (UI) was developed, which makes the watermarking process user friendly. The UI provides the sender with the freedom of opting cover image and watermark image in which actual data is stored. Confidential data can also be entered dynamically by the user. This is then made secure by symmetric key encryption technique and transmitted over the media. The transformation that is employed is the 3 level DWT process. The PSNR and MSE are also calculated to verify the robustness of the algorithm.

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“…Khare and Srivastava (2021) suggested a new image watermarking technique that yields high robustness against most conventional attacks by incorporating DWT, homomorphic transform (HT), and SVD (DWTHTSVD). The algorithm employed in this article (Abdulrahman & Ozturk, 2019;Budiman et al, 2017;Li et al, 2021;Veni & Meyyappan, 2017;Wang et al, 2020) makes watermarks far more robust by combining discrete cosine transform (DCT) and DWT, giving full play to the strengths of both with advantages over either one individually. In this article (Dhar et al, 2020;Su et al, 2017), Hessenberg decomposition is combined with nonsubsampled contourlet transform (NSCT) and non-nonsubsampled shearlet transform (NSST), which are more superior in resisting attacks such as rotation and shear.…”

Section: Introductionmentioning

confidence: 99%

Strong Robustness Watermarking Algorithm Based on Lifting Wavelet Transform and Hessenberg Decomposition

Lin

Wang

et al. 2022

International Journal of Web Services Research

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Watermark imperceptibility and robustness in the present watermarking algorithm based on discrete wavelet transform (DWT) could be weakened due to data truncation. To solve this problem, a strong robustness watermarking algorithm based on the lifting wavelet transform is proposed. First, the color channels of the original image are separated, and the selected channels are processed through lifting wavelet transform to obtain low-frequency information. The information is then split into blocks, with Hesseneberg decomposition performed on each block. Arnold algorithm is used to scramble the watermark image, and the scrambled watermark is transformed into a binary sequence that is then embedded into the maximum element of Hessenberg decomposed matrix by quantization modulation. The experimental results exhibit a good robustness of this new algorithm in defending against a wide variety of conventional attacks.

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DWT DCT based new image watermarking algorithm to improve the imperceptibility and robustness

Cited by 7 publications

References 2 publications

Machine Learning Based Digital Image Watermarking

Machine Learning Based Digital Image Watermarking

User Interface Based High Payload Double Image Steganography

Strong Robustness Watermarking Algorithm Based on Lifting Wavelet Transform and Hessenberg Decomposition

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