Pub Date : 1900-01-01DOI: 10.4018/978-1-7998-8386-9.ch001
E. Essefi
This chapter is meant to give the state of the art of forensic geochemistry and recent advances. In terms of forensic organic geochemistry, detecting mature organic matter including polluting hydrocarbons follows an experimental procedure by using recent experimental analytical techniques. However, the interpretation of these results needs an understanding of the geochemical context to make a distinction between the natural and the human made origin of oil. Infrared data coupled with statistical analyses would have an important relevance for the detection of the pollution during the Anthropocene, which is marked an increasing human pollution reaching the level of environmental crimes. In terms of nuclear and isotopic forensic geochemistry, recent studies provided that nuclear forensics considers the fact that some measurable parameters or signatures are distinctive.
{"title":"Advances in Forensic Geochemistry","authors":"E. Essefi","doi":"10.4018/978-1-7998-8386-9.ch001","DOIUrl":"https://doi.org/10.4018/978-1-7998-8386-9.ch001","url":null,"abstract":"This chapter is meant to give the state of the art of forensic geochemistry and recent advances. In terms of forensic organic geochemistry, detecting mature organic matter including polluting hydrocarbons follows an experimental procedure by using recent experimental analytical techniques. However, the interpretation of these results needs an understanding of the geochemical context to make a distinction between the natural and the human made origin of oil. Infrared data coupled with statistical analyses would have an important relevance for the detection of the pollution during the Anthropocene, which is marked an increasing human pollution reaching the level of environmental crimes. In terms of nuclear and isotopic forensic geochemistry, recent studies provided that nuclear forensics considers the fact that some measurable parameters or signatures are distinctive.","PeriodicalId":281747,"journal":{"name":"Technologies to Advance Automation in Forensic Science and Criminal Investigation","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127513485","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4018/978-1-7998-8386-9.ch007
Qingzhong Liu, Tze-Li Hsu
The detection of different types of forgery manipulation including seam-carving in JPEG images is a hot spot in image forensics. Seam carving was originally designed for content-aware image resizing. It is also being used for forgery manipulation. It is still very challenging to effectively identify the seam carving forgery under recompression. To address the highly challenging detection problems, this chapter introduces an effective approach with large feature mining. Ensemble learning is used to deal with the high dimensionality and to avoid overfitting that may occur with some traditional learning classifier for the detection. The experimental results validate the efficacy of proposed approach to detecting JPEG double compression and exposing the seam-carving forgery while the JPEG recompression is proceeded at the same quality and a lower quality, which is generally much harder for traditional detection methods. The methodology introduced in this chapter provides a strategy and realistic approach to resolve the highly challenging problems in image forensics.
{"title":"Large Feature Mining With Ensemble Learning for Image Forgery Detection","authors":"Qingzhong Liu, Tze-Li Hsu","doi":"10.4018/978-1-7998-8386-9.ch007","DOIUrl":"https://doi.org/10.4018/978-1-7998-8386-9.ch007","url":null,"abstract":"The detection of different types of forgery manipulation including seam-carving in JPEG images is a hot spot in image forensics. Seam carving was originally designed for content-aware image resizing. It is also being used for forgery manipulation. It is still very challenging to effectively identify the seam carving forgery under recompression. To address the highly challenging detection problems, this chapter introduces an effective approach with large feature mining. Ensemble learning is used to deal with the high dimensionality and to avoid overfitting that may occur with some traditional learning classifier for the detection. The experimental results validate the efficacy of proposed approach to detecting JPEG double compression and exposing the seam-carving forgery while the JPEG recompression is proceeded at the same quality and a lower quality, which is generally much harder for traditional detection methods. The methodology introduced in this chapter provides a strategy and realistic approach to resolve the highly challenging problems in image forensics.","PeriodicalId":281747,"journal":{"name":"Technologies to Advance Automation in Forensic Science and Criminal Investigation","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115871637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4018/978-1-7998-8386-9.ch005
Tushar Mane, A. Pawar
Deep learning-based investigation mechanisms are available for conventional forensics, but not for IoT forensics. Dividing the system into different layers according to their functionalities, collecting data from each layer, finding the correlating factor, and using it for pattern detection is the fundamental concept behind the proposed intelligent system. The authors utilize this notion for embedding intelligence in forensics and speed up the investigation process by providing hints to the examiner. They propose a novel cross-layer learning architecture (CCLA) for IoT forensics. To the best of their knowledge, this is the first attempt to incorporate deep learning into the forensics of the IoT ecosystem.
{"title":"Cross-Layer Learning","authors":"Tushar Mane, A. Pawar","doi":"10.4018/978-1-7998-8386-9.ch005","DOIUrl":"https://doi.org/10.4018/978-1-7998-8386-9.ch005","url":null,"abstract":"Deep learning-based investigation mechanisms are available for conventional forensics, but not for IoT forensics. Dividing the system into different layers according to their functionalities, collecting data from each layer, finding the correlating factor, and using it for pattern detection is the fundamental concept behind the proposed intelligent system. The authors utilize this notion for embedding intelligence in forensics and speed up the investigation process by providing hints to the examiner. They propose a novel cross-layer learning architecture (CCLA) for IoT forensics. To the best of their knowledge, this is the first attempt to incorporate deep learning into the forensics of the IoT ecosystem.","PeriodicalId":281747,"journal":{"name":"Technologies to Advance Automation in Forensic Science and Criminal Investigation","volume":"49 12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131923185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4018/978-1-7998-8386-9.ch006
Sachil Kumar, Anu Singla, Ruddhida R Vidwans
A fire investigation is a difficult and challenging task. An investigator's basic task at a fire scene is two-fold: first, to ascertain the origin of the fire and, second, to closely investigate the site of origin and try to determine what triggered a fire to start at or near that spot. Usually, an investigation would begin by attempting to obtain a general view of the site and the fire damage; this may be achieved at ground level or from an elevated location. Following this, one may examine the materials available, the fuel load, and the condition of the debris at different locations. Surprisingly, the science of fire investigation is not stagnant, and each year, more information to assist investigators in determining the location and cause of a fire by diligent observation of the scene and laboratory study of fire debris is released. This chapter is split into two sections. The first section discusses the general procedures to be used during a fire investigation, and the second section discusses laboratory analysis of ignitable liquid residue analysis.
{"title":"Fire Investigation and Ignitable Liquid Residue Analysis","authors":"Sachil Kumar, Anu Singla, Ruddhida R Vidwans","doi":"10.4018/978-1-7998-8386-9.ch006","DOIUrl":"https://doi.org/10.4018/978-1-7998-8386-9.ch006","url":null,"abstract":"A fire investigation is a difficult and challenging task. An investigator's basic task at a fire scene is two-fold: first, to ascertain the origin of the fire and, second, to closely investigate the site of origin and try to determine what triggered a fire to start at or near that spot. Usually, an investigation would begin by attempting to obtain a general view of the site and the fire damage; this may be achieved at ground level or from an elevated location. Following this, one may examine the materials available, the fuel load, and the condition of the debris at different locations. Surprisingly, the science of fire investigation is not stagnant, and each year, more information to assist investigators in determining the location and cause of a fire by diligent observation of the scene and laboratory study of fire debris is released. This chapter is split into two sections. The first section discusses the general procedures to be used during a fire investigation, and the second section discusses laboratory analysis of ignitable liquid residue analysis.","PeriodicalId":281747,"journal":{"name":"Technologies to Advance Automation in Forensic Science and Criminal Investigation","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116790875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4018/978-1-7998-8386-9.ch003
E. Essefi
Forensic sedimentology is a relatively recently realized field. Sedimentological methods used to solve cases have evolved as the field has developed, beginning with simple identification of minerals and progressing to the examination of individual grains using highly advanced scanning electron microscopes. More simple methods, such as color analysis, are still used today, but in addition, forensic sedimentologists look at surface textures and grain size distribution. For instance, quartz grains were used in a forensic technique as sediment fingerprint. The particle size distribution is one of the important tests when analysing sediments and soils in geological studies. For forensic work, the particle size distribution of sometimes very small samples requires precise determination using a rapid and reliable method with a high resolution. FRITSCH laser granulometer offers rapid and accurate sizing of particles in the range 0.04–2000 μm for a variety of sample types, including soils, unconsolidated sediments, dusts, powders, and other particulate materials.
{"title":"Advances in Forensic Sedimentology","authors":"E. Essefi","doi":"10.4018/978-1-7998-8386-9.ch003","DOIUrl":"https://doi.org/10.4018/978-1-7998-8386-9.ch003","url":null,"abstract":"Forensic sedimentology is a relatively recently realized field. Sedimentological methods used to solve cases have evolved as the field has developed, beginning with simple identification of minerals and progressing to the examination of individual grains using highly advanced scanning electron microscopes. More simple methods, such as color analysis, are still used today, but in addition, forensic sedimentologists look at surface textures and grain size distribution. For instance, quartz grains were used in a forensic technique as sediment fingerprint. The particle size distribution is one of the important tests when analysing sediments and soils in geological studies. For forensic work, the particle size distribution of sometimes very small samples requires precise determination using a rapid and reliable method with a high resolution. FRITSCH laser granulometer offers rapid and accurate sizing of particles in the range 0.04–2000 μm for a variety of sample types, including soils, unconsolidated sediments, dusts, powders, and other particulate materials.","PeriodicalId":281747,"journal":{"name":"Technologies to Advance Automation in Forensic Science and Criminal Investigation","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125316981","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4018/978-1-7998-8386-9.ch004
E. Essefi
This chapter aims to investigate advance and relevance of remote sensing in detecting the increasing transnational terrorist and crimes acts. This work should take into the widest definition of transnational crimes and terrorist activities and the link between. Geopolitics has created a favor climate for the setting of transnational crimes and terrorism at the Tunisian-Libyan borders. A possible future scenario is the fall of a military base with high technology arms in the hand of terrorist groups. Remote would be relevant by monitoring of terrorist mobility and their number evolution, arms quality and quantity within the base and the region, linked illegal activities funding terrorist groups (human trafficking from Africa to Europe, arms trade towards Mali, and smuggling).
{"title":"Advances of Forensic Remote Sensing Applications in the Face of Transnational Organized Crime and Terrorism","authors":"E. Essefi","doi":"10.4018/978-1-7998-8386-9.ch004","DOIUrl":"https://doi.org/10.4018/978-1-7998-8386-9.ch004","url":null,"abstract":"This chapter aims to investigate advance and relevance of remote sensing in detecting the increasing transnational terrorist and crimes acts. This work should take into the widest definition of transnational crimes and terrorist activities and the link between. Geopolitics has created a favor climate for the setting of transnational crimes and terrorism at the Tunisian-Libyan borders. A possible future scenario is the fall of a military base with high technology arms in the hand of terrorist groups. Remote would be relevant by monitoring of terrorist mobility and their number evolution, arms quality and quantity within the base and the region, linked illegal activities funding terrorist groups (human trafficking from Africa to Europe, arms trade towards Mali, and smuggling).","PeriodicalId":281747,"journal":{"name":"Technologies to Advance Automation in Forensic Science and Criminal Investigation","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130395428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4018/978-1-7998-8386-9.ch010
Md Ariful Haque, S. Shetty
Financial sectors are lucrative cyber-attack targets because of their immediate financial gain. As a result, financial institutions face challenges in developing systems that can automatically identify security breaches and separate fraudulent transactions from legitimate transactions. Today, organizations widely use machine learning techniques to identify any fraudulent behavior in customers' transactions. However, machine learning techniques are often challenging because of financial institutions' confidentiality policy, leading to not sharing the customer transaction data. This chapter discusses some crucial challenges of handling cybersecurity and fraud in the financial industry and building machine learning-based models to address those challenges. The authors utilize an open-source e-commerce transaction dataset to illustrate the forensic processes by creating a machine learning model to classify fraudulent transactions. Overall, the chapter focuses on how the machine learning models can help detect and prevent fraudulent activities in the financial sector in the age of cybersecurity.
{"title":"Leveraging Machine Learning in Financial Fraud Forensics in the Age of Cybersecurity","authors":"Md Ariful Haque, S. Shetty","doi":"10.4018/978-1-7998-8386-9.ch010","DOIUrl":"https://doi.org/10.4018/978-1-7998-8386-9.ch010","url":null,"abstract":"Financial sectors are lucrative cyber-attack targets because of their immediate financial gain. As a result, financial institutions face challenges in developing systems that can automatically identify security breaches and separate fraudulent transactions from legitimate transactions. Today, organizations widely use machine learning techniques to identify any fraudulent behavior in customers' transactions. However, machine learning techniques are often challenging because of financial institutions' confidentiality policy, leading to not sharing the customer transaction data. This chapter discusses some crucial challenges of handling cybersecurity and fraud in the financial industry and building machine learning-based models to address those challenges. The authors utilize an open-source e-commerce transaction dataset to illustrate the forensic processes by creating a machine learning model to classify fraudulent transactions. Overall, the chapter focuses on how the machine learning models can help detect and prevent fraudulent activities in the financial sector in the age of cybersecurity.","PeriodicalId":281747,"journal":{"name":"Technologies to Advance Automation in Forensic Science and Criminal Investigation","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114721894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4018/978-1-7998-8386-9.ch002
E. Essefi
Traditionally, forensic geophysics involves the study, search, localization, and mapping of buried objects or elements within soil, buildings, or water using geophysics tools for legal purposes. Recently, with the evolution of environmental crimes, forensic geophysics gave special care to detection, location, and quantification of polluting products. New techniques including the magnetic susceptibility have emerged to investigate this type of crimes. After discussing the state of the art of forensic geophysics, this chapter proposed the magnetic susceptibility as an efficient tool of environmental crimes detection. A case study of pollution detection was proposed from Tunisia. Being a fast and cheap technique, magnetic surveys represent a real promise for environmental forensic geophysics.
{"title":"Advances in Forensic Geophysics","authors":"E. Essefi","doi":"10.4018/978-1-7998-8386-9.ch002","DOIUrl":"https://doi.org/10.4018/978-1-7998-8386-9.ch002","url":null,"abstract":"Traditionally, forensic geophysics involves the study, search, localization, and mapping of buried objects or elements within soil, buildings, or water using geophysics tools for legal purposes. Recently, with the evolution of environmental crimes, forensic geophysics gave special care to detection, location, and quantification of polluting products. New techniques including the magnetic susceptibility have emerged to investigate this type of crimes. After discussing the state of the art of forensic geophysics, this chapter proposed the magnetic susceptibility as an efficient tool of environmental crimes detection. A case study of pollution detection was proposed from Tunisia. Being a fast and cheap technique, magnetic surveys represent a real promise for environmental forensic geophysics.","PeriodicalId":281747,"journal":{"name":"Technologies to Advance Automation in Forensic Science and Criminal Investigation","volume":"23 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132531858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4018/978-1-7998-8386-9.ch009
Chi-Hsuan Huang, Yu Sun, Chiou-Shana Fuh
In this chapter, an AI (artificial intelligence) solution for LPR (license plate recognition) on moving vehicles is proposed. The license plates in images captured with cameras on moving vehicles have unpredictable distortion and various illumination which make traditional machine vision algorithms unable to recognize the numbers correctly. Therefore, deep learning is leveraged to recognize license plate in such challenging conditions for better recognition accuracy. Additionally, lightweight neural networks are chosen since the power supply of scooter is quite limited. A two-stage method is presented to recognize license plate. First, the license plates in captured images are detected using CNN (convolutional neural network) model and the rotation of the detected license plates are corrected. Subsequently, the characters are recognized as upper-case format (A-Z) and digits (0-9) with second CNN model. Experimental results show that the system achieves 95.7% precision and 95% recall at high speed during the daytime.
{"title":"Vehicle License Plate Recognition With Deep Learning","authors":"Chi-Hsuan Huang, Yu Sun, Chiou-Shana Fuh","doi":"10.4018/978-1-7998-8386-9.ch009","DOIUrl":"https://doi.org/10.4018/978-1-7998-8386-9.ch009","url":null,"abstract":"In this chapter, an AI (artificial intelligence) solution for LPR (license plate recognition) on moving vehicles is proposed. The license plates in images captured with cameras on moving vehicles have unpredictable distortion and various illumination which make traditional machine vision algorithms unable to recognize the numbers correctly. Therefore, deep learning is leveraged to recognize license plate in such challenging conditions for better recognition accuracy. Additionally, lightweight neural networks are chosen since the power supply of scooter is quite limited. A two-stage method is presented to recognize license plate. First, the license plates in captured images are detected using CNN (convolutional neural network) model and the rotation of the detected license plates are corrected. Subsequently, the characters are recognized as upper-case format (A-Z) and digits (0-9) with second CNN model. Experimental results show that the system achieves 95.7% precision and 95% recall at high speed during the daytime.","PeriodicalId":281747,"journal":{"name":"Technologies to Advance Automation in Forensic Science and Criminal Investigation","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129742759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4018/978-1-7998-8386-9.ch008
Tzu-Yun Lin, Yu-Ru Wang
Image-related crimes cause the urgent demand for tracing the origin of digital images. The breakthrough is a passive detection method via photo response non-uniformity (PRNU) analysis proposed by Lukáš et al. Recently, digital images are often shot with handheld devices (such as smartphones) and transmitted using social media (such as LINE). Most of the images are distorted (such as compressed and resized) during transmission. Previous studies are less focused on the impact of transmission compression through social networks. Thirty-one different Apple mobile phones were used to capture digital images in the experiment. Images were uploaded to the photo album via LINE software and then downloaded. The modified signed peak correlation energy (MSPCE) statistics is used to evaluate the correlation between the PRNU values of the disputed images and the pattern noise of the experimental devices. Experimental results show that the PRNU analysis method can effectively trace the source of the shot device using the distorted images which are compressed and resized during the transmission in LINE.
{"title":"Forensic Camera Identification in Social Networks via Camera Fingerprint","authors":"Tzu-Yun Lin, Yu-Ru Wang","doi":"10.4018/978-1-7998-8386-9.ch008","DOIUrl":"https://doi.org/10.4018/978-1-7998-8386-9.ch008","url":null,"abstract":"Image-related crimes cause the urgent demand for tracing the origin of digital images. The breakthrough is a passive detection method via photo response non-uniformity (PRNU) analysis proposed by Lukáš et al. Recently, digital images are often shot with handheld devices (such as smartphones) and transmitted using social media (such as LINE). Most of the images are distorted (such as compressed and resized) during transmission. Previous studies are less focused on the impact of transmission compression through social networks. Thirty-one different Apple mobile phones were used to capture digital images in the experiment. Images were uploaded to the photo album via LINE software and then downloaded. The modified signed peak correlation energy (MSPCE) statistics is used to evaluate the correlation between the PRNU values of the disputed images and the pattern noise of the experimental devices. Experimental results show that the PRNU analysis method can effectively trace the source of the shot device using the distorted images which are compressed and resized during the transmission in LINE.","PeriodicalId":281747,"journal":{"name":"Technologies to Advance Automation in Forensic Science and Criminal Investigation","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124244505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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