Toolmarks

IMPRESSIONS TO ALGORITHMS: EVALUATING DEEP LEARNING METHODS FOR TOOLMARK MATCHING AND SOURCE ATTRIBUTION Sakthi Priyadharshini. K, 2nd Year LL.M (Crime and Forensic law), The Tamilnadu Dr.Ambedkar Law University, The School of Excellence in Law, Chennai (India) Download Manuscript doi.org/10.70183/lijdlr.2025.v03.154 Forensic science has historically relied heavily on tool mark analysis, which links tools to trace evidence by examining impression imprints. However, traditional methods continue to rely on examiner-driven visual comparison, which raises ongoing questions about reproducibility, transparency, and the validity of evidence in court. New advances in computer vision and deep learning are changing this field by providing unbiased, data-driven methods for source identification and tool mark categorization. With a focus on convolutional and contrastive architectures, this study explores the potential of advanced neural network models for automated similarity evaluation, classification, and likelihood ratio estimation in forensic tool mark evidence. The investigation demonstrates that multivariate neural networks routinely outperform conventional correlation-based and statistical approaches, producing higher accuracy and lower error rates, using carefully selected datasets of consecutively made tools and fired cartridge cases. Extensive data augmentation and interpretability frameworks minimize key technical issues, such as feature extraction under varying angles and substrates and dataset restrictions. According to benchmarking data, deep learning significantly improves the ability to distinguish between impressions from the same source and those from distinct sources, achieving over 95% sensitivity and specificity. Beyond performance, the study outlines paths for digital traceability and explainable AI integration, emphasizing the significance of algorithmic transparency, validation standards, and trial admissibility. The results provide useful suggestions for forensic professionals, legislators, and developers, advancing the transition from subjective examiner assessments to repeatable, algorithm-driven attribution. In the end, this work establishes the foundation for tool mark analysis in contemporary forensic practice that is more dependable, systematic, and legally defendable.

COMPARATIVE ANALYSIS OF TRADITIONAL AND MODERN TECHNIQUES IN TOOLMARKS: A FORENSIC SCIENCE PERSPECTIVE Aswathy N C, LLM, Crime and Forensic Law, The Tamil Nadu Dr. Ambedkar Law University, School of Excellence in Law, Chennai (India) Download Manuscript doi.org/10.70183/lijdlr.2025.v03.153 Toolmark examination has been a mainstay of forensic research for more than a century, developing from simple visual inspection methods to sophisticated, technologically assisted analytical procedures. This study offers a thorough comparative analysis of both conventional and contemporary methods for toolmark identification, highlighting their practical usefulness in contemporary forensic investigations, methodological underpinnings, accuracy parameters, and inherent difficulties. Modern analytical techniques include advanced imaging technologies, 3D surface analysis, statistical modeling, and automated comparison systems to improve objectivity and reproducibility, whereas traditional methods mostly rely on the examiner’s skill and visual comparison using microscopes. The study emphasizes that rather than substituting one for the other, the most dependable results in forensic toolmark analysis come from combining traditional knowledge with contemporary technology developments. By strengthening accuracy, efficiency, and judicial dependability, this hybrid approach creates a more scientifically sound foundation for forensic toolmark analysis in the twenty-first century.