Digital video watermarking using Hybrid wavelet transform with Cosine, Haar, Kekre, Walsh, Slant and Sine transforms

Kulkarni, Tejas S.; Dewan, Jaya H.

doi:10.1109/iccubea.2016.7860055

Cited by 4 publications

(4 citation statements)

References 6 publications

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“…Classic methods can include methods, such as those based on the least significant bit [ 23 , 24 , 25 ] or methods based on frequency domain manipulation (discrete wavelet transform, discrete Fourier transform, discrete sine, and cosine transform) [ 10 , 26 , 27 , 28 , 29 , 30 , 31 ]. There are many modifications of these methods, such as a combination of the least significant bit method and those based on manipulation of the frequency domain, which includes publications concerning hybrid domains [ 23 , 32 , 33 , 34 ].…”

Section: Literature Reviewmentioning

confidence: 99%

High-Quality Video Watermarking Based on Deep Neural Networks for Video with HEVC Compression

Kaczyński

Piotrowski

Pietrow

2022

Sensors

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This article presents a method for transparent watermarking of high-capacity watermarked video under H.265/HEVC (High-Efficiency Video Coding) compression conditions while maintaining high-quality encoded image. The aim of this paper is to present a method for watermark embedding using neural networks under conditions of subjecting video to lossy compression of the HEVC codec using the YUV420p color model chrominance channel for watermarking. This paper presents a method for training a deep neural network to embed a watermark when a compression channel is present. The discussed method is characterized by high accuracy of the video with an embedded watermark compared to the original. The PSNR (peak signal-to-noise ratio) values obtained are over 44 dB. The watermark capacity is 96 bits for an image with a resolution of 128 × 128. The method enables the complete recovery of a watermark from a single video frame compressed by the HEVC codec within the range of compression values defined by the CRF (constant rate factor) up to 22.

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Section: Literature Reviewmentioning

confidence: 99%

High-Quality Video Watermarking Based on Deep Neural Networks for Video with HEVC Compression

Kaczyński

Piotrowski

Pietrow

2022

Sensors

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“…Sensors 2022, 22, x FOR PEER REVIEW 3 of 21 frequency domain [18][19][20][21][22][23], which are characterized by better survival despite various types of conversions than are those generated via the method of the least significant bit. The modification of the most convenient areas takes place in different methods.…”

Section: Presentation Of the Concept Of The Proposed Methodsmentioning

confidence: 99%

“…The classical methods include such methods as the least significant bit method [ 9 ], the mask method [ 17 ], and frequency-domain manipulations (discrete cosine transform [ 18 ], discrete sine transform [ 19 ], discrete wavelet transform [ 10 ], discrete Fourier transform [ 20 ], and hybrid domains [ 21 , 22 , 23 ], among others). Assorted variations of the above-mentioned methods are used to embed watermarks in both static images and videos.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The least significant bit method [ 9 , 32 ], as its name implies, uses the least significant bit of each channel value of a given colour palette, affecting the image slightly, but it is prone to losing the watermark due to all kinds of conversions. The whole image is also influenced by methods that manipulate the frequency domain [ 18 , 19 , 20 , 21 , 22 , 23 ], which are characterized by better survival despite various types of conversions than are those generated via the method of the least significant bit. The modification of the most convenient areas takes place in different methods.…”

Section: Literature Reviewmentioning

confidence: 99%

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High-Quality Video Watermarking Based on Deep Neural Networks and Adjustable Subsquares Properties Algorithm

Kaczyński

Piotrowski

2022

Sensors

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This paper presents a method of high-capacity and transparent watermarking based on the usage of deep neural networks with the adjustable subsquares properties algorithm to encode the data of a watermark in high-quality video using the H.265/HEVC (High-Efficiency Video Coding) codec. The aim of the article is to present a method of embedding a watermark in a video with HEVC codec compression by making changes in a video in a way that is not noticeable to the naked eye. The method presented here is characterised by focusing on ensuring the accuracy of the original image in relation to the watermarked image, providing the transparency of the embedded watermark, while ensuring its survival after compression by the HEVC codec. The article includes a presentation of the practical results of watermark embedding with a built-in variation mechanism of its capacity and resistance, thanks to the adjustable subsquares properties algorithm. The obtained PSNR (peak signal-to-noise ratio) results are at the level of 40 dB or better. There is the possibility of the complete recovery of a watermark from a single frame compressed in the CRF (constant rate factor) range of up to 16, resulting in a BER (bit error rate) equal to 0 for the received watermark.

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Optimal Information Hiding: Advanced Bitstream‐Based Watermarking for Secure and Efficient Integration of Image Data in Digital Videos

Ramyashree,

P. S.,

et al. 2024

Journal of Engineering

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Video watermarking is a technique used to embed a visible or invisible mark onto a video file in order to claim ownership, protect intellectual property rights, and detect unauthorized users. The issue of video piracy arises due to the advancement of Internet services and the utilization of various storage technologies. This problem becomes particularly significant with the widespread propagation of media through online channels. The constant progress in sharing of data, videos, images, and applications necessitates the implementation of measures focused on security and the protection of intellectual ownership rights. To address this issue, we proposed a novel Bitstream video watermarking technique that integrates cutting‐edge methodologies to seamlessly embed image data within digital videos. This technique stands apart from less sophisticated watermarking methods by capitalizing on spatial and temporal domain concepts in video processing. An experiment is conducted to analyze the time required for the insertion and extraction processes to gain insights into the performance of bitstream watermarking in the video. While enhancing the existing approach, we systematically explored diverse strategies aimed at augmenting watermark capacity, bolstering robustness, and heightening visual imperceptibility. These strategies encompassed the intricate infusion of watermark fragments into the frame area through chaotic mechanisms, the integration of error‐correcting codes to ensure data fidelity, and the selective omission of dynamic video segments during information extraction processes. Rigorous experimentations are undertaken by the constraints of lossy compression scenarios, maintaining equivalent or reduced bitrate parameters. The findings substantiate the favorable outcomes stemming from the implemented enhancement, affirming the tangible suitability of the phase watermarking technique for the protection of video copyrights. In order to assess the quality of the bitstream video watermarking technique, the peak signal‐to‐noise ratio (PSNR), structural similarity index (SSIM), and normalized correlation coefficient (NCC) are computed and examined by the testing data. The suggested model achieved a PSNR value up to 32.52 dB, SSIM value up to 0.97, and NCC value is 1. The dynamic range (DR) and spatial resolution (SR) are measured in the evaluation process. Security is enhanced significantly after implementing the multifaceted approach. The watermarking scheme is more robust and can effectively resist the potential attacks. In summary, this study advances the field of video watermarking by proposing a sophisticated bitstream technique and rigorously evaluating its performance. The outcomes underscore its practical viability for copyright protection, contributing to a more secure digital media landscape.

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Digital video watermarking using Hybrid wavelet transform with Cosine, Haar, Kekre, Walsh, Slant and Sine transforms

Cited by 4 publications

References 6 publications

High-Quality Video Watermarking Based on Deep Neural Networks for Video with HEVC Compression

High-Quality Video Watermarking Based on Deep Neural Networks for Video with HEVC Compression

High-Quality Video Watermarking Based on Deep Neural Networks and Adjustable Subsquares Properties Algorithm

Optimal Information Hiding: Advanced Bitstream‐Based Watermarking for Secure and Efficient Integration of Image Data in Digital Videos

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