A Multi-scene Automatic Classification and Grading Protection and Supervision Method for Image Sensitive Information

In the digital era, image data has become a key asset in various fields such as transportation, government affairs, education, and more. Despite national policies and regulations like the “Data Security Law” and “Data Classification and Grading Standards” providing a legal framework for data management, the existing classification and grading standards do not offer specific guidance for image data. This leads to unique challenges in managing and protecting image data in practical applications. This study proposes a set of methods for the automatic classification and graded protection of sensitive image data, aiming to fill the current gap in image data management. By analyzing the needs of government affairs, transportation, and education scenarios, this paper establishes a set of image data classification and grading standards applicable to multiple scenarios. The study employs machine learning technologies, especially the multi-head attention mechanism, to efficiently identify sensitive or security-related information within image data and accurately classify and grade it accordingly. Furthermore, this paper explores the application of blockchain technology in the regulation of image data access and use, proposing a novel data protection mechanism. Experimental results show that the method proposed in this paper for the automatic classification, graded protection, and supervision of sensitive image data across different scenarios not only fills a theoretical gap but also demonstrates excellent application potential and practical value in practice. It provides an effective solution for the protection and supervision of sensitive image data in multiple scenarios.