Application of Deep Learning in Digital Evidence Collection and Authentication

This paper explores the application of deep learning technology in the field of digital evidence collection and authentication. The study focuses on the use of deep learning in image, video, audio, text, and network traffic analysis, and analyzes the main challenges faced, including data insufficiency and interpretability issues. The paper introduces the design of deep learning-based digital evidence analysis systems, including system architecture, core algorithms, and performance optimization strategies. Through case studies, particularly in obscene content detection and minor facial recognition, the paper demonstrates the high accuracy and efficiency of deep learning models. The findings indicate that deep learning technology has brought revolutionary changes to digital evidence analysis, significantly improving accuracy and efficiency, and providing strong support for maintaining social security and judicial fairness.