Video Watermarking Algorithm Based on NSCT, Pseudo 3D-DCT and NMF

Fan, Di; Zhang, Xiao; Kang, Wenshuo; Zhao, Huiyuan; Lv, Yingjun

doi:10.3390/s22134752

Cited by 13 publications

(8 citation statements)

References 30 publications

(45 reference statements)

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“…This publication will present a system for protecting ownership of video materials based on embedding a transparent watermark in the video. In the case of transparent image watermarking, there are descriptions in the literature of more elementary methods, such as the use of the least significant bit [ 9 ], based on frequency analysis, such as the wavelet transform [ 10 ], as well as more complex methods [ 11 ] that are a specific combination of basic methods or even dedicated to particular video codecs and use their characteristics for their operation. With the popularization of artificial neural networks (ANNs), watermark embedding methods using them have also emerged [ 12 , 13 , 14 , 15 , 16 , 17 ].…”

Section: Introductionmentioning

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: Introductionmentioning

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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“…PSNR, short for peak signal-to-noise ratio, is a full-reference image quality evaluation index, which is formulated as Equations ( 19) and (20),…”

Section: Imperceptibility and Capacitymentioning

confidence: 99%

“…Finally, the algorithm was made robust against temporal asynchrony attacks. Fan et al [20] proposed a video watermarking algorithm by combining non-subsampled contourlet transform, 3D-DCT, and non-negative matrix factorization (NMF) to embed the encrypted 2D copyright watermark into the base matrix of NMF. However, although these algorithms achieve good robustness, most of them require geometric synchronization when attacked and are more time-consuming due to the embedding in the transform domain.…”

Section: Introductionmentioning

confidence: 99%

A Robust Video Watermarking Algorithm Based on Two-Dimensional Discrete Fourier Transform

Yang,

Zhang,

Jiao

et al. 2023

Electronics

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Due to the continuous development and popularity of digital video technology, the copyright protection of digital video content has become an increasingly prominent issue. Digital video watermarking, as an effective means of digital copyright protection, has attracted widespread attention from academia and industry. The two-dimensional discrete Fourier transform (2D-DFT) template-based method has the advantages of good real-time performance and robustness, but the embedding capacity is small and cannot resist frame-dropping attack. To address this problem, a new template construction method is proposed in this paper, which can extend the embedding capacity from 1 bit each group of pictures (GOP) to theoretically n bits each GOP. In addition, by changing the GOP pattern, the method gains the ability to resist frame-dropping attacks. Experimental results show that the proposed method can achieve larger watermark capacity while maintaining strong robustness against image-processing attacks, geometric attacks, video-processing attacks and compression attacks.

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“…The subject of this publication is the embedding of a watermark in a video; therefore, it should be noted that there are various methods for this purpose, both classical [ 13 , 14 , 15 , 16 , 39 , 40 , 41 , 42 , 43 ] and those based on artificial neural networks [ 11 , 12 ]. Among the works describing the process of embedding a watermark in a video, there are some dedicated specifically to particular video codecs, such as H.264/AVC (Advanced Video Coding) [ 13 , 14 , 39 , 40 , 41 ] and its successor H.265/HEVC (High-Efficiency Video Coding) [ 15 , 16 , 42 , 43 ].…”

Section: Literature Reviewmentioning

confidence: 99%

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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Video Watermarking Algorithm Based on NSCT, Pseudo 3D-DCT and NMF

Cited by 13 publications

References 30 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

A Robust Video Watermarking Algorithm Based on Two-Dimensional Discrete Fourier Transform

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

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