Screen-Shooting Resilient Watermarking Scheme via Learned Invariant Keypoints and QT

Li, Li; Bai, Rui; Zhang, Shanqing; Chang, Chin‐Chen; Shi, Mengtao

doi:10.3390/s21196554

Cited by 19 publications

(9 citation statements)

References 47 publications

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“…When discussing the embedding of a watermark in an image, a distinction should be made between embedding a watermark in a static image [ 23 , 34 , 35 , 36 , 37 ] and in a video [ 14 , 15 , 16 , 17 ]. The methods used for static images find their application and development in methods for video purposes.…”

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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“…To enhance the performance, they proposed the flip-based watermarking scheme in [20]. Li et al [22] proposed a scheme by using quaternion discrete fourier transform (QDFT) and tensor decomposition (TD). In the scheme, feature regions filtering model to superpoint (FRFS) algorithm is applied to locate the watermark embedding regions, then the QDFT and TD algorithm are used to embed watermark bits into the cover image.…”

Section: The Transform Invariant Domain-based Methodsmentioning

confidence: 99%

A screen‐shooting resilient document image watermarking scheme using deep neural network

Fei

Xia

et al. 2022

IET Image Processing

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With the advent of the screen‐reading era, the confidential documents displayed on the screen can be easily captured by a camera without leaving any traces. Thus, this paper proposes a novel screen‐shooting resilient watermarking scheme for document image using deep neural network. By applying this scheme, when the watermarked image is displayed on the screen and captured by a camera, the watermark can be still extracted from the captured photographs. Specifically, the scheme is an end‐to‐end neural network with an encoder to embed watermark and a decoder to extract watermark. During the training process, a distortion layer between encoder and decoder is added to simulate the distortions introduced by screen‐shooting process in real scenes, such as camera distortion, shooting distortion, and light source distortion. Furthermore, a background sensitive loss and a lpips loss are used to improve visual quality of the watermarked document images in the training process. Besides, a strength factor adjustment strategy is also designed to improve the visual quality with little loss of bit extraction accuracy. The experimental results show that the proposed scheme has higher visual quality and robustness than the other two recent state‐of‐the‐art methods.

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“…In the literature, there are many publications on embedding a watermark in a static image using the classical methods [ 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 ]. These methods use the whole image or some of its regions, constituting the areas of interest.…”

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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Screen-Shooting Resilient Watermarking Scheme via Learned Invariant Keypoints and QT

Cited by 19 publications

References 47 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 screen‐shooting resilient document image watermarking scheme using deep neural network

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

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