Research and exploration of digital watermarking technology based on BP neural network

Janyan Chen

doi:10.1117/12.2682367

26 May 2023 Research and exploration of digital watermarking technology based on BP neural network

Janyan Chen

Proceedings Volume 12700, International Conference on Electronic Information Engineering and Data Processing (EIEDP 2023); 127001Q (2023) https://doi.org/10.1117/12.2682367
Event: International Conference on Electronic Information Engineering and Data Processing (EIEDP 2023), 2023, Nanchang, China

Abstract

Digital watermarking is a technique to embed specific digital signals into digital products to protect copyright integrity, which is an effective way to protect information security. Neural network technology is a mathematical model generation technology that mimics the structural function of biological neural networks and has good self-learning and merit-seeking capabilities. It is feasible to add neural network to digital watermarking technology for copyright protection research. In this paper, we select and improve a generative adversarial network from a classical neural network to generate adversarial samples required for subsequent research using watermarked datasets, where the improvements include combining high-level loss and low-level feature loss and using adversarial labels to facilitate adversarial classification. The improved generated adversarial samples have the advantage of less alteration to the original image. In this paper, new digital mosaic techniques combined with neural networks are explored in depth rain for new big data directions, which are informative for new areas of research.

Citation Download Citation

Janyan Chen "Research and exploration of digital watermarking technology based on BP neural network", Proc. SPIE 12700, International Conference on Electronic Information Engineering and Data Processing (EIEDP 2023), 127001Q (26 May 2023); https://doi.org/10.1117/12.2682367

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KEYWORDS

Digital watermarking

Detection and tracking algorithms

Education and training

Target detection

Neural networks

Data modeling

Convolution

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