Machine learning based blind color image watermarking scheme for copyright protection

Sinhal, Rishi; Jain, Deepak Kumar; Ansari, Irshad Ahmad

doi:10.1016/j.patrec.2021.02.011

Cited by 60 publications

(24 citation statements)

References 33 publications

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“…For the ownership verification and copyright protection of digital color images, Sinhal et al 71 proposed a low-cost, blind watermarking scheme. The image is first converted into a YCbCr model using a selection of 4 × 4 blocks of Y-component randomization.…”

Section: Others Perspectivesmentioning

confidence: 99%

Comprehensive review of watermarking techniques in deep-learning environments

Singh

2022

J. Electron. Imag.

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Recently, the demand for the generation, sharing, and storage of massive amounts of multimedia information-especially in the form of images-from different intelligent devices and sensors has increased drastically. This introduces issues including the illegal access and fraudulent usage of this information as well as other security concerns. Watermarking consists of embedding a watermark design in a digital cover and then later extracting it to provide a solution for ownership conflict and copyright violation issues involving the media data. Presently, in watermarking, the use of deep-learning approaches is incredibly beneficial due to their accuracy, superior results and strong learning ability. We present a comprehensive review of watermarking techniques in deep-learning environments. We start with basic concepts of traditional and learning-based digital watermarking; we later review the popular deep-learning model-based digital watermarking methods; then, we summarize and compare the most recent contribution in the literature; finally, we highlight obfuscation challenges and further research directions.

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Section: Others Perspectivesmentioning

confidence: 99%

Comprehensive review of watermarking techniques in deep-learning environments

Singh

2022

J. Electron. Imag.

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“…To make the image key points have strong robustness and rotation invariance characteristics, it is necessary to determine the direction parameters of the key points according to the gradient distribution characteristics of the pixels: 19,20 E Q -T A R G E T ; t e m p : i n t r a l i n k -; e 0 2 0 ; 1 1 6 ; 1 7 1 mðx; yÞ…”

Section: Fusion Hmmmentioning

confidence: 99%

(Retracted) Multimedia music image production combining HMM and song feature tags

Cui

Wang

2022

J. Electron. Imag.

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In the vast river of art and music, this wonderful flower of art has inherited culture through its expansion in horizontal space and continuation in vertical time since its birth. This research mainly discusses the filming of multimedia music images that integrates hidden Markov model (HMM) and song feature tags. Hidden Markov model is a statistical model based on hidden Markov process, which is mainly used for modeling stochastic processes. The editing structure, editing style, and the time and space performance of the editing should be considered in the editing process. When filming multimedia music images in the study of music history, there are not only the shooting pictures of the main body of the musical things but also the introduction of the environmental information generated by the musical things. Editing needs to consider whether each shot and each picture meets the actual research situation for the musical things to be described. It uses HMM to predict the song category based on the user's listening behavior records and song characteristics and then recommends the corresponding category of music to the user in real-time. Finally, the recommended results of several algorithms are verified on the experimental data set. It compares the difference between the traditional music algorithm and the HMM from different dimensions, such as accuracy, error rate, and recommended time efficiency. The accuracy of HMM for music is predicted to be 95%. The multinote recognition system of the piano uses the characteristics of the hidden Markov double random process to describe the statistical characteristics of the audio stream of notes. The proposed research will help promote the dissemination of music image information.

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“…In [16], a new model of inverse DCT kernel for high-efficiency video coding was proposed. In [17], the authors presented a blind and robust scheme using YCbCr color space, integer wavelet transform, and DCT for color image watermarking. Some studies on blocking artifacts have also been proposed.…”

Section: Related Workmentioning

confidence: 99%

Improving the Quality of Left-Behind Children’s Participation in Sports through Wireless Network Monitoring

Zhao

2021

Mobile Information Systems

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Urbanization is the process that people shift from rural to urban areas, which has led to large numbers of left-behind children in China. The left-behind children stay in rural regions of China while their parents work in urban areas. The left-behind children have few opportunities to participate in sports due to the lacking of concern, and it is not of high quality even though they participate in sports. Therefore, it is necessary to improve the quality of left-behind children's sports participation through wireless network monitoring. Wireless network monitoring transmits high-definition (HD) video streaming in real time to facilitate feedback timely. This paper studies the two-dimensional (2D) integer discrete cosine transform (DCT) and analyzes the reason for image distortion, then an improved DCT coefficient quantization approach is proposed for long-distance real-time transmission of HD video streaming, and a noise processing with a zero-mean noise processing is added in optimized approach to solve the image distortion problem. The experimental results show that the proposed improved approach has a good performance in reducing the blocking artifacts, and within the image reconstruction, the proposed approach improves the subjective video quality.

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Machine learning based blind color image watermarking scheme for copyright protection

Cited by 60 publications

References 33 publications

Comprehensive review of watermarking techniques in deep-learning environments

Comprehensive review of watermarking techniques in deep-learning environments

(Retracted) Multimedia music image production combining HMM and song feature tags

Improving the Quality of Left-Behind Children’s Participation in Sports through Wireless Network Monitoring

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