Machine Learning in Forensic Applications

Carriquiry, Alicia L.; Hofmann, Heike; Tai, Xiao Hui; VanderPlas, Susan

doi:10.1111/j.1740-9713.2019.01252.x

Cited by 37 publications

(24 citation statements)

References 7 publications

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“…When a bullet is fired, it is ignited within the chamber and travels through the barrel of the firearm. Since bullets are intentionally designed to be wider than the barrel of the firearm, as the bullet passes through the barrel, the barrel compresses, thereby leaving toolmarks on the bullet [7], [8]. These toolmarks become a ''ballistic fingerprint'' that examiners can use to identify specific characteristics of the firearm that discharged the bullet [9].…”

Section: A Backgroundmentioning

confidence: 99%

Automated Firearm Classification From Bullet Markings Using Deep Learning

et al. 2020

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Firearm violence is one of the leading causes of death in many countries around the world, including Thailand. This work proposes a fast and accurate automated method to classify firearm brands from bullet markings. Specifically, a panoramic image of a bullet collected from a crime scene was captured using a developed mobile phone application and custom-built portable hardware. The top three state-of-the-art CNNs pretrained on ImageNet-DenseNet121, ResNet50, and Xception-were further trained on the same training set, which was composed of 718 bullets collected from eight different firearm brands-Beretta, Browning, CZ, Glock, Norinco, Ruger, Sig Sauer, and Smith & Wesson-using a five-fold cross validation technique. DenseNet121 provided the highest AUC of 0.99 for CZ classification (the most common registered firearm brand in Thailand) and the highest average AUC for the eight firearm brands (0.9780 ± 0.0130 SD), which was significantly higher than those of ResNet50 and Xception. In addition, there were no interaction effects between the CNN model and firearm brand on AUC. DenseNet121, which had the highest AUC, was evaluated on the test set (72 bullets), and the results showed that the Beretta and CZ classifications had the lowest accuracy (91.18%), followed by the Browning and Norinco classifications (96.88%), whereas the Glock, Ruger, Sig Sauer, and Smith & Wesson classifications had the highest accuracy (98.41%). These results suggest that the developed mobile phone application based on a deep learning algorithm and the custom-built portable hardware have promising potential for use at crime scenes to classify firearms from bullet markings. By narrowing down the list of suspects, this convenient approach can potentially accelerate bullet identification processes for many forensic science examiners. INDEX TERMS Forensic science, automated firearm classification, 9 mm bullet marking, densely connected convolutional network.

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Section: A Backgroundmentioning

confidence: 99%

Automated Firearm Classification From Bullet Markings Using Deep Learning

et al. 2020

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“…Not only is it a very time-consuming and expensive task, but it also exposes image analysts to sensitive and disturbing data on a daily basis, which can affect their emotional state and consequently their performance. Hence, the development of fast, automatic and efficient tools for the automated discovery and analysis of images and videos to be implemented in criminal laboratories becomes crucial for the forensic field [2,3].…”

Section: Introductionmentioning

confidence: 99%

Assessment and Estimation of Face Detection Performance Based on Deep Learning for Forensic Applications

Chaves

Fidalgo

Alegre

et al. 2020

Sensors

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Face recognition is a valuable forensic tool for criminal investigators since it certainly helps in identifying individuals in scenarios of criminal activity like fugitives or child sexual abuse. It is, however, a very challenging task as it must be able to handle low-quality images of real world settings and fulfill real time requirements. Deep learning approaches for face detection have proven to be very successful but they require large computation power and processing time. In this work, we evaluate the speed–accuracy tradeoff of three popular deep-learning-based face detectors on the WIDER Face and UFDD data sets in several CPUs and GPUs. We also develop a regression model capable to estimate the performance, both in terms of processing time and accuracy. We expect this to become a very useful tool for the end user in forensic laboratories in order to estimate the performance for different face detection options. Experimental results showed that the best speed–accuracy tradeoff is achieved with images resized to 50% of the original size in GPUs and images resized to 25% of the original size in CPUs. Moreover, performance can be estimated using multiple linear regression models with a Mean Absolute Error (MAE) of 0.113, which is very promising for the forensic field.

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“…The automatic detection, segmentation and recognition of text in natural images, also known as text spotting, is a challenging task with multiple practical applications [ 1 , 2 , 3 ]. The location and transcription of text may be a great aid in forensic applications such as the analysis of Child Sexual Abuse Material (CSAM), the investigation of domains of the Tor network or the retrieval of critical information from criminal scenes among other tasks [ 4 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

“…Manual text-spotting is not only a very time-consuming and expensive task, but it also exposes analysts to sensitive data on a daily basis, affecting their emotional state, therefore hindering their performance. Thus, the development and implementation of fast, automatic and efficient tools for the analysis of images and videos become crucial for the forensic field [ 4 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

Rectification and Super-Resolution Enhancements for Forensic Text Recognition

Blanco‐Medina

Fidalgo

Alegre

et al. 2020

Sensors

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Retrieving text embedded within images is a challenging task in real-world settings. Multiple problems such as low-resolution and the orientation of the text can hinder the extraction of information. These problems are common in environments such as Tor Darknet and Child Sexual Abuse images, where text extraction is crucial in the prevention of illegal activities. In this work, we evaluate eight text recognizers and, to increase the performance of text transcription, we combine these recognizers with rectification networks and super-resolution algorithms. We test our approach on four state-of-the-art and two custom datasets (TOICO-1K and Child Sexual Abuse (CSA)-text, based on text retrieved from Tor Darknet and Child Sexual Exploitation Material, respectively). We obtained a 0.3170 score of correctly recognized words in the TOICO-1K dataset when we combined Deep Convolutional Neural Networks (CNN) and rectification-based recognizers. For the CSA-text dataset, applying resolution enhancements achieved a final score of 0.6960. The highest performance increase was achieved on the ICDAR 2015 dataset, with an improvement of 4.83% when combining the MORAN recognizer and the Residual Dense resolution approach. We conclude that rectification outperforms super-resolution when applied separately, while their combination achieves the best average improvements in the chosen datasets.

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Machine Learning in Forensic Applications

Abstract: The 2009 National Academy of Sciences report found pattern‐evidence disciplines to be rife with subjectivity. In the decade since, machine learning methods have been developed to try to address that issue. By Alicia Carriquiry, Heike Hofmann, Xiao Hui Tai and Susan VanderPlas.

Cited by 37 publications

References 7 publications

Automated Firearm Classification From Bullet Markings Using Deep Learning

Automated Firearm Classification From Bullet Markings Using Deep Learning

Assessment and Estimation of Face Detection Performance Based on Deep Learning for Forensic Applications

Rectification and Super-Resolution Enhancements for Forensic Text Recognition

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