The application of electronic evidence in forensic imaging analysis
Dan Wu, Xiangbin Zuo, Yuanyuan Guo · Journal of Radiation Research and Applied Sciences · 2025
Objective In the modern judicial system, especially in forensic image analysis, electronic evidence is becoming increasingly important. This article focuses on the application of advanced algorithms and technologies to improve the efficiency and accuracy of electronic evidence in this field. Methods In this study, an image analysis framework based on deep learning is proposed, where key features of the images are automatically extracted using convolutional neural networks (CNN), and an enhancement algorithm is designed for blurred or low-resolution images. In the experiments, X-ray images, CT scans, and video clips were selected for testing. Results The results show that the algorithm improves the accuracy of image analysis, achieving a detection accuracy of 96.5 % in binary classification tasks and maintaining an accuracy of 84.7 % even under high noise conditions (SNR = 5 dB). Additionally, the processing time was significantly reduced, with a training time of only 3.2 h and an inference time of 45 ms per sample. The algorithm successfully identifies key evidence in actual criminal cases. After analysis, it is found that the algorithm has high robustness and stability in complex scenes. Conclusion Future research work will explore more efficient algorithm models and extend them to related fields.