A Human-Al Collaborative Framework For Forensic Video Evidence Analysis In Law Enforcement

Abstract

The rapid growth of digital surveillance systems has led to an unprecedented increase in video data used in criminal investigations, making forensic video analysis a critical component of modern law enforcement. However, traditional manual analysis methods are time-consuming, error-prone, and inefficient when handling large-scale data. While artificial intelligence (AI) techniques such as deep learning have improved automation and detection accuracy, they often lack interpretability, contextual reasoning, and legal reliability. This paper proposes a human–AI collaborative framework for forensic video evidence analysis that integrates machine intelligence with expert validation. The framework employs Discrete Fourier Transform (DFT) for feature extraction and a Support Vector Machine (SVM) optimized using Bayesian Optimization for classification of manipulated and authentic video frames. Human-in-the-loop validation ensures improved reliability and reduces false interpretations. Experimental results demonstrate high performance, achieving 94.68% accuracy and strong precision-recall balance. The proposed approach enhances transparency, efficiency, and trustworthiness, making it highly suitable for real-world forensic applications and legal admissibility.