Abstract
Since the artistry of the work cannot be accurately described, the identification of reproducible plagiarism is more difficult. The identification of reproducible plagiarism of digital image works requires in‐depth research on the artistry of artistic works. In this paper, a remote judgment method for plagiarism of painting image style based on wireless network multitask learning is proposed. According to this new method, the uncertainty of painting image samples is removed based on multitask learning algorithm edge sampling. The deep‐level details of the painting image are extracted through the multitask classification kernel function, and most of the pixels in the image are eliminated. When the clustering density is greater than the judgment threshold, it can be considered that the two images have spatial consistency. It can also be judged based on this that the two images are similar, that is, there is plagiarism in the painting. The experimental results show that the discrimination rate is always close to 100%, the misjudgment rate of plagiarism of painting images has been reduced, and the various indicators in the discrimination process are the lowest, which fully shows that a very satisfactory discrimination result can be obtained.
Highlights
In the commercial society, the competition in the cultural industry has increased fiercely, and commercial imitation and plagiarism may have a direct impact on the economic interests of both parties [1]
This paper proposes a remote judgment method of painting image style plagiarism based on wireless network multitask learning
This paper proposes a remote judgment method of painting image style plagiarism based on wireless network multitask learning strategy
Summary
The competition in the cultural industry has increased fiercely, and commercial imitation and plagiarism may have a direct impact on the economic interests of both parties [1]. The detection accuracy (or performance) of these methods is not so satisfactory To address these issues, we propose BinDeep, a deep learning approach for binary code similarity detection. We propose BinDeep, a deep learning approach for binary code similarity detection This method firstly extracts the instruction sequence from the binary function and uses the instruction embedding model to vectorize the instruction features. This paper proposes a remote judgment method of painting image style plagiarism based on wireless network multitask learning. When the cluster density is greater than the threshold value, the two images can be considered to have spatial consistency It can be judged based on this that the two images are similar, that is, there is plagiarism in the painting
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
