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Artificial Intelligence in Forensic Science: Current Applications and Future Direction
Kecerdasan Buatan dalam Sains Forensik: Aplikasi Semasa dan Hala Tuju Masa Depan
NOOR HAZFALINDA HAMZAH, SIM LI XUAN, GINA FRANCESCA GABRIEL, KHAIRUL OSMAN
& NUR MAHIZA MD ISA
ABSTRACT
Since its inception in the 1940s, artificial intelligence (AI) has bloomed and all modern electronic devices integrates AI in its system. Also known as machine intelligence, AI is the cross-disciplinary study of machines to understand, mimic and replicate human mental and neural processes: visual perception, decision making, pattern recognition, speech, learning and many other cognitive processes. In forensic science, AI is used as decision support tool, besides being used in databases that aid in investigations including Combined DNA Index System (CODIS), Integrated Ballistics Identification System (IBIS) and the United Kingdom National DNA Database (NDNAD). With the rise of cyber-crime globally, AI is especially crucial in digital forensics as it is needed in investigations, acquisition, preservation and recovery of digital data. Big data is another subset of AI which could bring radical advancement in forensic fields for pattern recognition, geographical categorization, automated reasoning, and digital forensics. However, despite AI applications enhancing forensic science, there are many issues such as legal regulation and interoperability that needs to be solved so that the potential of AI in forensics can be maximized.
Keywords: Artificial intelligence, digital forensics, big data, forensic science
ABSTRAK
Sejak ditemui pada 1940-an, kecerdasan buatan (AI) telah berkembang dan semua peranti elektronik moden telah mengintegrasikan AI dalam sistemnya. AI juga dikenali sebagai kecerdasan mesin yang merupakan kajian merentas disiplin mesin untuk memahami, meniru dan mereplikasi proses pemikiran dan saraf manusia: persepsi visual, membuat keputusan, pengecaman corak, pertuturan, pembelajaran dan banyak proses kognitif yang lain.
Dalam sains forensik, AI digunakan sebagai sokongan kepada keputusan, selain digunakan dalam pangkalan data yang membantu dalam penyiasatan seperti Sistem Indeks DNA Gabungan (CODIS), Sistem Identifikasi Balistik Bersepadu (IBIS) dan Pangkalan Data DNA Kebangsaan United Kingdom (NDNAD). AI amat penting dalam forensik digital di seluruh dunia kerana ia diperlukan dalam penyiasatan, perolehan, pemeliharaan dan pemulihan data digital, terutama dengan peningkatan jenayah siber di seluruh dunia. Data raya adalah satu subset AI yang boleh membawa kemajuan radikal dalam bidang forensik, terutama dalam pengecaman corak, pengkategorian geografi, penaakulan automatik dan forensik digital. Walaupun aplikasi AI mempertingkatkan sains forensik, masih terdapat banyak isu seperti undang-undang dan saling kendali yang perlu diselesaikan supaya potensi AI dalam forensik dapat dimaksimumkan.
Kata kunci: Kecerdasan Buatan, forensik digital, data raya, sains forensik
INTRODUCTION
Artificial intelligence (AI), also known as machine or computer intelligence, is the cross-disciplinary study of machines to understand, mimic and replicate human mental and neural processes:
visual perception, decision making, pattern recognition, speech, learning and many other cognitive processes (Chen et al. 2008; Nilsson 2014). Intelligence is hallmarked by a capability to attain and apply knowledge and skills (Ribaux
2007). AI combines various principles and techniques in computational science, logics, mathematics, mechanics and biology to achieve the ultimate goal of teaching machines intelligence, in order for machines to be capable of doing things currently only humans can do (Ertel 2017; Frankish
& Ramsey 2014; Rich & Knight 1991).
Since its inception in the 1940s, AI has bloomed and all modern electronic devices today integrates AI in their systems to a certain degree.
AI has broad application in various fields, from
linguistics, economics (Spangler et al. 2007), engineering, neurobiology, psychology and so on, in the effort to generate the optimum solutions to problems, big and small, faced in that particular field, in a realistic time-frame (Ertel 2017; Mitchell 2014; Nilsson 2014).
AI is an umbrella for multiple subsets: case- based reasoning (CBR), machine learning, deep learning, data mining, Bayesian network, natural language processing, reinforcement learning and hybrid systems, rule-based learning and many more (Chen et al. 2008; Ertel 2017; Jiang et al. 2017).
AI often weave in various techniques to categorize unstructured data and present the information in simplified, understandable interface. Among all these subdivisions, there are a few AI techniques that have gained traction in forensic science applications while AI branches like data visualization and robotics are less relevant to the medico-legal field at the present.
APPLICATION OF AI IN FORENSIC SCIENCE
In a crime scene investigation, there is a lot of information to be processed; any notable evidences are unearthed and analyzed in investigation process.
The investigator has to make three major decisions in crime investigation: availability of information, conclusion from available evidence and narrowing down of suspects (Canter 2000). The investigative process is heavily influenced by quality and admissibility of data, such as modus operandi, victim details, scene evidence and so on. One issue in forensics is that the investigator could have confirmatory bias, forming conclusions based on preconceived ideas instead of meticulous analysis (Oskamp 1965). After scrutinizing all the available evidences and establishing associations, the investigator has to generate suspects, which is the most vital part of an investigation that carries the most operational and ethical issues (Townsley &
Pease 2002).
In addition, defense lawyers always question and lambaste forensic courtroom testimony for lacking scientific backing. As law enforcers reason, infer and deduce on the basis of experience and approximations, the conclusions lack robustness and reproducibility, and there is no safeguard against prejudice and falsifications (Kassin 2013).
On the other hand, statistical evidence is frequently misconstrued in court due to communication gap between scientists, investigators and legal practitioners, thus causing judicial error (Chinnikatti 2018).
Due to the large amount of data, long turnaround time and possible cognitive fallacies, computer science methods are used to aid crime investigation (Oatley et al. 2006). Artificial
intelligence presents an interesting solution to the intricacies in criminal investigations. By combining statistics and complex computational algorithms, AI could simplify complex data into a language understandable by all parties involved and reduce the turnaround time. Essentially, machine learning algorithms are used to identify regularities in the available data and classify the pattern into classes, which then may be used to draw inferences and predictions. It could also be used to model scenarios to support expert testimony, or evaluate expert and judicial opinions (Chinnikatti 2018).
Consequently, deep learning, machine learning and case based reasoning act as decision support tools for forensic professionals (Mujtaba et al. 2016;
Yeow et al. 2014).
One of the earliest state-of-the-art AI tool in forensics is the Combined DNA Index System (CODIS) (Federal Bureau of Investigation 2005;
Panneerchelvam & Norazmi 2003), the DNA database the Federal Bureau of Investigation of United States of America used to match to crime scene deoxyribonucleic acid (DNA). The United Kingdom’s National DNA Database (NDNAD) (Uk Home Office 2019) is a similar national database to CODIS at a larger scale, with the addition of familial screening. In Malaysia, the Forensic DNA Database Malaysia (FDDM) is used in cases involving DNA analysis. The algorithm aligns crime scene DNA to offender DNA profile in the repository, generating hits that would enable the police force to narrow down suspects.
Case based reasoning (CBR) is another AI approach in forensic science as a support system in decision-making and reasoning. The goal of CBR machine is to make a conclusion with minimal human interference. CBR finds best-fit case within collection of past cases with a set of metric. If there is a previous situation that is congruent with the present case, CBR will apply past knowledge to the current condition. If there is a case that is not completely matched but similar enough, CBR may adapt previous decisions to the current situation (Mitchell 2014). This simulates expert opinion in that forensic experts rely on experiences to draw inferences based on current evidences. An alternative Intelligent Forensic Autopsy Report System (I-AuReSys) was designed to suggest possible cause of death with machine learning (Yeow et al. 2014). Machine learning coupled with CBR can be used to predict cause of death from autopsy reports up to 78.25% accuracy (Mujtaba et al. 2016).
Bayesian network is a computational technique interlinked with statistics that infers probabilistic graphical scenarios based on causal relationships. It essentially visualizes probabilities of contributory relationships between different hypotheses and evidences based on Bayes’ theorem, thereby providing legal professionals and forensic scientists
a quantitative metric in assessing investigative hypotheses and understanding mutual dependencies between complex events (Constantinou et al. 2015;
Ertel 2017; Fenton et al. 2003). Constantinou et al.
(2015) and team derived a decision support system for evaluating potentially violent psychiatric patients to improve risk management using Bayesian networks [21]. Bayesian network can be introduced to fingerprint analysis in assigning numerical weight to evidence, communicating the distinctiveness of evidence to the judge or jury (Mohamad Noor et al. 2014; Neumann et al. 2012).
Bayesian network is also integrated into ballistic investigations to facilitate bullet comparisons. Integrated Ballistics Identification System (IBIS) is a system designed in Canada to automate cartridge and bullet identification with known firearms. IBIS carries out pattern recognition on crime scene cartridges or bullets and compares them to database, generating candidates for further evaluation based on breech face pattern, firing pin marks, extractor impressions, ejector
marks and so on. Bayesian networks in IBIS is developed by using deep learning algorithms paired with logical constraints from datasets, enabling IBIS to extract patterns from raw crime scene image data and generate a relative similarity score with the database (Morris et al. 2016).
Transition from manual ballistics comparison methods to IBIS has improved the throughput of Boston Police Department significantly, as witnessed by the 523% increase of cold hits generated monthly (Braga & Pierce 2004; Braga &
Pierce 2011). Recently, Patrick Pursley who was wrongly convicted of first-degree murder 20 years ago was exonerated based on retested IBIS evidence. The ballistics comparison system indicated that unique minutiae on evidence shell casings do not match test-fired cartridges in the retrial, allowing Pursley to be acquitted of his initial charges after two decades (Moreno 2018).
Table 1 summarises examples of machine learning techniques used in forensic science available today.
TABLE 1 Examples of machine learning techniques used in forensic science
Technique Field
Case based reasoning Forensic pathology (Mujtaba et al. 2016; Yeow et al.
2014)
Bayesian network Forensic psychology (Constantinou et al. 2015) Fingerprint analysis (Federal Bureau of Investigation 2005; Mohamad Noor et al. 2014; Neumann et al.
2012; Panneerchelvam & Norazmi 2003) Ballistics (Morris et al. 2016)
AI AND DIGITAL FORENSICS
Digital forensic concerns investigations, acquisition, preservation and recovery of digital data, which should not be mistaken with computational forensics (Constantini et al. 2019; Yeow et al.
2014). Digital forensics includes network investigations, such as firewall or web server security broach. Another niche in digital investigations is in cloud or online databases, which are online platforms for storage. As cloud platforms stores data across different geographic area, ownership jurisdiction is a critical matter in these investigations. Due to the universality of digital devices, such as computers, laptops, smart phones, tablets and so forth, a major part of criminal investigations include computer and mobile devices.
Since the development of the World Wide Web in the 1990s, cybercrime has increased exponentially. With the continual proliferation of digital technology across the globe, criminals use progressively more advanced equipment to
perpetrate sophisticated crime. Due to the mammoth volumes of data evidence and storage capacity, current procedures in digital investigations could not keep up with the magnitude of retrieved information, creating a backlog in digital forensic analysis. The pervasiveness of electronic storage and popularity of dissimilar storage systems causes a challenge in digital forensic analysis (Mitchell 2014; Yeow et al.
2014).
The most important issue in digital investigation is the location of potential evidence.
Investigators have to rummage through sizable data to locate evidence, including social media accounts and physical devices such as computer processing unit and hard disk. Secure technologies in use across all electronic devices poses an additional complication in forensic investigation. Network communication, complex encryption, encrypted systems, end-to-end encryption and anonymous routing all complicate the investigator’s work. In the event that the evidence is extracted, it would not be obvious which evidence is admissible in
court (Irons & Lallie 2014). Generally, there is no one universal practice in digital forensic investigation, with varying operating procedures across organizations and countries.
These technical issues necessitated the use of machine intelligence in digital analysis. When artificial intelligence methodology is applied to digital forensics, the volume and speed issue would be eliminated, while maximizing efficiency in solving complex data (Iron & Mallie 2014). In order to filter through the huge volumes of data in digital devices, information retrieval algorithm is used to expedite the extraction data extraction (Beebe et al. 2011). Hash algorithms are used in computer forensics to sieve irrelevant dormant files from investigations. There are multiple string matching algorithms used in forensic analysis:
brute force, Boyer-Moore, Karp-Rabin, Horspool, Quick Search. Raita et cetera. Scalpel (Richard Iii
& Roussev 2005) is one of the most widely used open source carving application employed in raw file extraction and analysis that drastically reduces an investigator’s workload. In combinations of different intelligent techniques, data anomaly can be captured, which includes unusual behavior, abnormal disk sector, formatted disc packets, personal relational data.
Parallel to physical forensic investigations, digital forensics would benefit from criminal profiling. Lai et al. (2013) came up with a structure to profile internet pirates, incorporating observable online footprint, behavior, and personal details (Lai et al. 2013). The Forensic Zachman Framework (FORZA) (Ieong 2006) provides a structured operating procedure for digital investigations, which enables complex and in depth investigations, identifying how, when and where crime happens.
Accordingly, a branch in digital forensics that deals with network systems uses scientific techniques to collect, examine, analyze, and document digital evidences from digital sources and network security outlooks to unearth evidences of cyber- crimes. Analyzation of massive amount network data and elucidating attack methods, behavior and event reconstruction are the primary complications in network forensics. Attack intention analysis thus provides leads and investigative directions to the analyst (Rasmi & Jantan 2013).
BIG DATA AND DATA MINING IN FORENSIC SCIENCE
Big data is generally defined as high volume, three- dimensional information with multiple variables that requires time to be analyzed (Baro et al. 2015;
Kitchin 2013; Leary 2013). Clinical, biological, social, consumer, imaging data and so on are obtained in real time, asynchronously or synchronously, in various formats (Lefèvre 2018).
Processing of these data requires multidimensional and intricate analyzation algorithm (Irons & Lallie 2014). In short, big data is any at-scale data that meets either or all three criterion: volume, variety, velocity (Zikopoulos et al. 2012). Big data is inextricably linked with data mining, an area of AI that makes use of statistical data analysis. Data mining systems detect and extract useful patterns from high volume data, which must be easily understandable, reproducible and novel.
Big data has ushered in new insights in data- scarce fields. The most common practice of big data is applied by the international technological companies, such as Google, Apple, Facebook, and Amazon. They collect consumer data online in cloud and utilize multiples data to optimize their business operations. Google harnessed real time information to fine-tune search engine performances and advertisement strategy algorithm to suit the demographic and geolocation needs of users. The rapid growth of social networks increases the consumer profile data available, which enables platforms like Facebook to adapt marketing strategies to fit user profile (Lefèvre 2018).
In recent years, big data is increasingly used in biomedical and biological sciences. AI techniques are used to process big data to formulate more precise, prognostic and personalized medicine. One such success is the electronic patient health record (EHR) containing medical, genetics, treatment and demographic information which enabled post- market surveillance of medications for improvement of pharmacovigilance in the United Kingdom (Leff & Yang 2015). Genome-wide association studies (GWAS) carrying high genomic data input facilitates discovery of novel therapies, finding infrequent genomic loci variants that present as therapeutic targets with noteworthy impact on the disease or trait (Denny et al. 2018).
As seen by big data applications in biomedicine, one could extrapolate that forensic science would benefit from big data as well. As forensic data are hard to come by, and even so they are highly unique, the court might question the basis of an expert opinion. A big data tactic can be used to collect and analyze data that would be otherwise unattainable: classical clinical, epidemiological, genomic mass data can be reused or analyzed with machine learning methods to provide new paradigm in forensic science (Lefevre et al. 2015). For instance, haplogroup data could better represented with the population data from GWAS studies, which would narrow down suspect pool in DNA analysis.
Another example of big data application is in forensic criminology. Vaughn et al. (2010) demonstrated that the 1 % of fire setters in the United States have unique demographic, behavioral and psychiatric traits using National Epidemiologic
Survey on Alcohol and Related Conditions (NESARC) data (Vaughn et al. 2010). The offenders experience typically substance use problems, behavioral disorders and display family history of antisocial behavior. With these kind of information, policy makers can design preventive and rehabilitative programs for criminals with greater success rates.
The police force has mined big data in meaningful way using geographical information systems (GIS). Crime mapping manipulates and process special crime records and summarizes them visually with variable parameters. Besides the visualization of crime hotspots, the system allows the user to cluster the geography by certain crime features, such as modus operandi or stolen property (Williamson et al. 2001). This practice gives an edge in strategizing criminal search. In Europe, Amsterdam police forces extended the basic GIS with decision tress and back propagation neural networks to acquire basic profiling, ascertain organizational patterns and predict criminal behavior (Williamson et al. 2001).
Constantini et al. (2019) proposed automated reasoning for the evidence analysis stage of investigations. A draft for functional decision support system used answer set programming to automate evidence analysis. Image tamper detection or forgery is a part of digital investigations, in which image falsification can be detected through watermark or signature, quality assessment, noise pattern or source identification.
Gupta et al. (2018) uses energy deviation measure in measuring pixel intensity relative to surrounding environment, improving accuracy of image extraction and identification (Gupta et al. 2018).
LIMITATIONS AND FUTURE DIRECTIONS
There are numerous obstacles in applying AI to forensics, mainly being legal repercussions and scarcity of data. Unfortunately, data in the justice system is not readily available to researchers due to legal reasons. Biometric data such as fingerprints, facial, stature et cetera naturally require authorizations for research purposes. Database such as the United States’ National Crime Information Center (NCIC) that has over 12 million criminal information, which could provide insight in criminal profiling and forensic epidemiology, is restricted to government officials. Similarly, CODIS is government regulated and not accessible by researchers (Delisi 2018).
Despite the emerging data deluge, access to big data is restricted because they are primarily collated by government and private companies. On the other hand, privacy and anonymity remains a chief concern in big data collection and analyzation.
With current AI technologies, information is constantly logged onto the short and long-term memory of website servers. The information remains accessible in the system locally even if the user had erased them, which presents a loophole in privacy and data protection (Villaronga et al. 2017).
Undoubtedly, big data is going to be a vital player in forensic science in the coming years. Despite its potential, big data present a myriad of epistemological, ethical and security questions that needs to be answered systematically.
Most existing intelligent forensics are localized to local operations, for example DNA databases like CODIS and NDNAD are only accessible in the United States and United Kingdom respectively. The downside of this is amalgamation of all available data for exchange of information is not possible due to lack of interphases. Interoperability, therefore, remains a crucial issue in forensic science computations (Meuwly 2012; Plomp & Grijpink 2011). The ten most commonly used data mining algorithms are non-standard and mostly research-based, which complicates multilateral operations at international scale (Wu et al. 2008). Coordinating heterogeneous infrastructure, legislation, organizations proves challenging locally, much less on international platforms. Even with collaborations in place, combination of EU national fingerprint and DNA database and exchange of biometric info through Interpol remains a feat. As global crime rate rises, the need for interoperable intelligence systems are needed especially to assist with investigations of multi-national syndicates.
With the huge variety of computational approach and techniques, there are no standardized operating procedure in forensic investigations, thus having no way to ascertain credibility of any methodologies (Karabiyik 2015). To solve this, we need to define universal minimum forensic science standards, starting from collection, processing and delivery of forensic data that adheres to Daubert Criteria. The benchmark proof algorithms should be internationally peer reviewed and highly reproducible in order to ensure robustness and reliability of AI methodologies across borders.
Regular exchange opportunities is needed to facilitate information exchange in related disciplines, with an example being International Well and Control Workshop. Any methods that could potentially be deployed extensively by law enforcement should be examined comprehensively and tested for error rate, in order to uphold the quality of evidence.
Currently, the chief applications of AI is still in digital forensics, and fingerprint analysis. We need to expand the application of AI, particularly in fields it shows promise in, for instance forensic pathology, criminology, epidemiology, biometric data and forensic engineering (Delisi 2018;
Holický et al. 2013; Vaughn et al. 2010). Many intelligent systems proposed in literature are yet to be adopted as legal solutions: face photo-sketch recognition (Galea & Farrugia 2017), cyber-crime profiling (Arthur et al. 2008; Rasmi & Jantan 2013) decision support (Constantini et al. 2019), biometrics (Dinerstein et al. 2007), DNA analyses (Mohamad Noor et al. 2014), sexual predation identification (Mcghee et al. 2011) and many more prototypes. Another point to consider is that system training require large amount of data which is simply unavailable in data-deficit fields at the current climate. This is an impedance to system modelling and machine learning, besides presenting difficulties in ascertaining efficiency and reliability in medico-legal settings.
CONCLUSION
Despite heavy assimilation of artificial intelligence in digital forensics, use of machine intelligence in forensic sciences is still lackadaisical. Forensic experts are still using traditional methods that are restrictive in certain aspects, such as data analyzation. However, it is encouraging that scientists and legal professionals are slowly recognizing the benefits of AI applications. We need corporation between different specialists to maximize the potential of machine intelligence in forensics, especially in the fast-growing digital forensics. There is a dire need to move past current limitations of forensic tools in use, and to augment use of accessible resources and develop more robust algorithms in order to improve efficiency in forensic investigations. Along with the exciting advances in AI and computational forensics, modern crime investigation shall benefit greatly from the hybrid-intelligence of humans and machines.
ACKNOWLEDGEMENT
This study was fully funded by Faculty Health Sciences, Universiti Kebangsaan Malaysia, project code GUP-2020-052.
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Noor Hazfalinda Hamzah Gina Francesca Gabriel Khairul Osman
Center for Diagnostic, Therapeutic and Investigative Studies
Faculty of Health Science Universiti Kebangsaan Malaysia Jalan Raja Muda Abdul Aziz
50300 Wilayah Persekutuan Kuala Lumpur Malaysia
Sim Li Xuan
Forensic Science Program Faculty of Health Science Universiti Kebangsaan Malaysia Jalan Raja Muda Abdul Aziz
50300 Wilayah Persekutuan Kuala Lumpur Malaysia
Nur Mahiza Md Isa
Dept. of Veterinary Pathology and Microbiology Faculty of Verterinary Medicine
Universiti Putra Malaysia Malaysia
Corresponding author: Noor Hazfalinda Hamzah E-mail: [email protected]
Tel: 0122767280 Fax: 03-92897602
Received: 24 December 2021 Revised: 4 October 2022
Accepted for publication: 4 November 2022
