Mr. Gang Zheng Profile

Gang Zheng

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Proceedings Article | 10 September 2024 Paper

Research on intellectual property protection algorithm of deep semantic segmentation model

Na Chen, Ying Li, Gang Zheng

Proceedings Volume 13257, 1325713 (2024) https://doi.org/10.1117/12.3040856

KEYWORDS: Digital watermarking, Image segmentation, Data modeling, Semantics, Performance modeling, Intellectual property, Education and training, Neural networks, Multimedia, Statistical modeling

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In its judicial interpretation, the Supreme People's Court clarified that algorithmic information can be protected by trade secrets by way of enumeration, which reflects the increased importance of algorithmic trade secret protection. Deep models have high intellectual property value and commercial value, and have even become the core competitiveness of some individuals or small businesses, making them the target of attacks by some malicious competitors and lawless elements. Current digital watermarking technology has been extended to deep neural networks, but these methods are only suitable for protecting the intellectual property of classification models. Therefore, this article proposes research on the intellectual property protection of semantic segmentation models. This paper abandons the traditional method of selecting trigger sets in classification model protection, and proposes an adversarial generation method to independently design trigger sets, embed patterns or symbols with special markings into pictures, and then use a backdoor mechanism to embed trigger set digital watermarks into semantic segmentation In the model, the backdoor disadvantages of the model are converted into backdoor advantages, making the segmentation model more discriminative during the verification process.

Proceedings Article | 8 June 2023 Paper

Research on personalized recommendation algorithms for the web with knowledge representation

Na Chen, Ying Li, Gang Zheng

Proceedings Volume 12707, 127074G (2023) https://doi.org/10.1117/12.2681370

KEYWORDS: Tunable filters, Head, Feature extraction, Machine learning, Education and training, Data modeling, Semantics, Systems modeling, Model-based design, Genetic algorithms

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With the rapid development of information technology and the increasing scale of the Internet, a huge amount of data and information has been generated, and people face a huge challenge to get the information they need from it. In order to solve these challenges, personalized recommendation technology has emerged, which can actively recommend items of potential interest to users. The most mainstream personalized recommendation technology is collaborative filtering, which has been applied in various fields and achieved good results. However, its recommendation performance tends to drop sharply when facing data sparsity and cold-start problems. Currently, knowledge representation techniques have attracted wide attention from academia and industry, and have been applied to recommender systems and other fields, and have made important breakthroughs. To solve the problem of data sparsity and improve recommendation accuracy, this paper introduces knowledge representation into neural collaborative filtering model and proposes a neural collaborative filtering model assisted by knowledge graph embedding. By alternating the training of the knowledge representation module of the recommendation module, the knowledge representation module assists the training of the recommendation module, which effectively improves the rating prediction effect. Through experiments, it is shown that the model not only improves 9.46% and 10.18% in MAE and RMSE respectively over the UserCF method, but also effectively alleviates the data sparsity problem.

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