Explainable Artificial Intelligence for Digital Forensics: Opportunities, Challenges and a Drug Testing Case Study

Forensic analysis is typically a complex and time-consuming process requiring forensic investigators to collect and analyse different pieces of evidence to arrive at a solid recommendation. Our interest lies in forensic drug testing, where evidence comprises a multitude of experimentally obtained data from samples (e.g. hair or nails), occasionally combined with questionnaire data, with a goal of quantifying the likelihood of drug use. The availability of intelligent data-driven technologies can support holistic decision-making in such scenarios, but this needs to be done in a transparent fashion (as opposed to using black-box models). To this end, this book chapter investigates the opportunities and challenges of developing interactive and eXplainable Artificial Intelligence (XAI) systems to support digital forensics and automate the decision-making process to enable fast and reliable generation of evidence for the court of law. Relevant XAI techniques and their applications in forensic testing, including feature section, missing data handling, XAI for multi-criteria and interactive learning, are discussed in detail. A case study on a forensic science company is used to demonstrate the real challenges of forensic reporting and potential for making use of forensic data to pave the way for future research towards XAI-driven digital forensics

Ambient Ionization: Surface Analysis of Sexual Assault Evidence and 2-Dimensional Imaging of Whole-Body Zebra Fish (Danio Rerio) Using Desorption Electrospray Ionization

Desorption electrospray ionization (DESI) is an ambient surface analysis technique capable of producing 2D and 3D images. The ionization mechanism utilizes a pneumatically assisted sprayer to deposit a charged solvent onto a sample surface. Subsequent impacting primary droplets produce ejecting surface secondary droplets containing desolved analytes, which are then detected by a mass analyzer. This thesis explores two fields of application of DESI, forensics and biological tissue analysis. The former involves the analysis of sexual assault evidence, in the form of condoms, lubricants, and their residues as a potential aid in convicting perpetrators. The latter focuses on investigating the potential use of the zebra fish (Danio rerio) as a model vertebrate organism for future toxicological and biological research. Whole-body 2D images were created, highlighting areas of interest such as, the brain, spinal cord, liver, and body fat

3D Face Reconstruction: the Road to Forensics

3D face reconstruction algorithms from images and videos are applied to many fields, from plastic surgery to the entertainment sector, thanks to their advantageous features. However, when looking at forensic applications, 3D face reconstruction must observe strict requirements that still make its possible role in bringing evidence to a lawsuit unclear. An extensive investigation of the constraints, potential, and limits of its application in forensics is still missing. Shedding some light on this matter is the goal of the present survey, which starts by clarifying the relation between forensic applications and biometrics, with a focus on face recognition. Therefore, it provides an analysis of the achievements of 3D face reconstruction algorithms from surveillance videos and mugshot images and discusses the current obstacles that separate 3D face reconstruction from an active role in forensic applications. Finally, it examines the underlying data sets, with their advantages and limitations, while proposing alternatives that could substitute or complement them

Innovations in latent fingerprint analysis for forensic applications with matrix assisted laser desorption/ionization mass spectrometry imaging

This dissertation presents work that aims to address the current limitations of latent fingerprint analysis in the forensic science field and discuss innovative new ways that matrix assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) could be used to develop techniques that would alleviate some of the current issues. The first chapter consists of a general introduction to MALDI-MSI and presents a general workflow for a MALDI-MSI experiment. The sixth and final chapter summarizes the work presented in this dissertation and provides a future outlook. The second chapter discusses the compatibility of MALDI-MSI and one of the most common forensic development techniques for latent fingerprints, cyanoacrylate fuming. An array of endogenous and exogenous compounds were studied to determine if there were any changes in structure (reactions with cyanoacrylate) or signal due to the fuming process. None of the compounds exhibited any structural changes and most had comparable signal intensity with or without cyanoacrylate fuming. One class of compounds, however, quaternary ammonium derivatives (present in many hygiene products) had significantly suppressed signal after fuming. The third chapter studies the cyanoacrylate fuming mechanism in more depth based on evidence from the mass spectra in the compatibility study. Specifically, several peaks were identified that were determined to be unique to the spectra of fingerprints that had been cyanoacrylate fumed. The peaks were identified by exact mass and MS/MS and were found to be dimers and trimers of ethyl cyanoacrylate. In addition, some further studies were done to determine which endogenous compounds are responsible for the adherence of the cyanoacrylate polymer to the fingerprint ridges. It was determined that the most polymer formation happens on fatty acids and amino acids, which must play an important role in the fuming process. The fourth chapter outlines how endogenous fingerprint compounds diffuse from the fingerprint ridges over time. The initial idea was to model the diffusion of a triacylglycerol (TG) in an attempt to determine the time since deposition or age of the fingerprint. It was thought that a TG would diffuse more slowly than fatty acids (FAs), which had been researched previously by another group, and would allow for aging over a longer time period. However, it was determined that the surface interactions between fingerprint compounds and the sample substrate played a larger role in the diffusion rate than the molecular weight of the compounds. For example, the more hydrophobic TG only diffused slower than the FA on a hydrophobic surface. The fifth chapter discusses using variability in the TG profile to determine differences in diet, exercise, and whether or not an individual has diabetes. As TGs play a role in many health conditions, including obesity and diabetes, differences could be reflected in the TG profile of a latent fingerprint. 79 total participants (16 with diabetes) were recruited to determine if the TG profile was impacted by diabetes. General trends showed the possibility that diabetics, particularly type 2 diabetics, could have higher levels of saturated TGs; however, no significant conclusions could be drawn due to diet and exercise differences obscuring some of the effects. Diet and exercise effects were tested with subsets of the original data set with clear diet and exercise habits. The exercise study included 8 male (4 that exercise regularly and 4 that do not) and 8 female participants (5 that exercise regularly and 3 that do not). Male participants that exercised regularly had a much lower relative abundance of completely saturated TGs compared to those that do not exercise. The same effect did not occur with the female participants. The study on diet consisted of 5 vegetarians, 3 low carbohydrate/ketogenic, and 4 people without any diet restrictions. The vegetarians had very high relative amounts of saturated TGs compared to either of the diets, whereas the ketogenic diet was comparable to the control

Surface-enhanced Raman spectroscopy for forensic analysis of human semen

Identification of an unknown stain encountered at a crime scene, especially where the context of the case does not provide an indication to the identity of the stain, currently requires a number of time consuming and costly presumptive and confirmatory tests to be performed. Surface-enhanced Raman spectroscopy (SERS) is a vibrational spectroscopic method that could allow crime scene analysts to identify unknown stains rapidly both in the laboratory and in the field. The SERS technique utilizes a laser, which interacts with molecules applied to a gold nanoparticle chip (SERS substrate) that enhances the normal Raman signal, producing a shift in energy characteristic of the vibrational modes present. Therefore, the light scattering spectrum obtained provides the analyst with a unique spectral fingerprint of the molecular components of the sample. The advantages of this SERS based method include its high sensitivity, speed, non-destructive nature, ease-of-use, minimal sample preparation requirement, portability, and multiplexing capabilities. In contrast to conventional Raman spectroscopy, SERS offers higher sensitivity resulting in small sample volumes (approximately 1 μL or less) being required for sample identification and the ability to process dilute solutions. This allows for the remaining sample to be used for other forensic tests, making the technique an ideal analytical method for use at a crime scene. vi It is hypothesized that SERS can be coupled with multivariate statistical methods, such as principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) to be established as a confirmatory technique in the forensic analysis of human body fluids. It was concluded that semen produces a spectral pattern that is consistent and readily distinct from blood, saliva, urine and vaginal fluid. In addition, this investigation identified and characterized semen from four donors, utilizing liquid semen as well as semen stains on cotton swatches and glass cover slips. Reproducibility was established by analyzing three separate SERS chips for every sample and/or solution. Ten spectra of each chip were obtained, averaged, and then compared to one another. A protocol was designed for the extraction of dried semen stains on cotton swatches and application to a SERS chip. Different extraction conditions were performed, varying both the volume of water used and the time the cutting remained submerged in the water, resulting in optimal signal from 5 μL of water for 5 minutes. Additional parameters including analysis of the perimeter of the stain and the use of saline as an extractant were examined. A second protocol for the extraction of dried semen stains from a glass cover slip was designed and tested, utilizing 1 μL of water. All experimental spectra were subjected to PCA for comparison with neat semen, and determined to be consistent. Additionally, a mixture of semen and vaginal fluid was evaluated. Visual inspection and PCA of the resulting spectra demonstrated that the mixture was a combination of both body fluids. Such samples are of particular importance in sexual assault cases. vii In summary, this preliminary study of the identification of semen using SERS demonstrates the potential for the method to be used as an investigative tool for the detection of trace amounts of human body fluids at crime scenes and within forensic laboratories. Not only is semen differentiable from other body fluids, but it is also capable of being extracted from stains and successfully identified by SER

A Comprehensive Analysis of the Role of Artificial Intelligence and Machine Learning in Modern Digital Forensics and Incident Response

In the dynamic landscape of digital forensics, the integration of Artificial Intelligence (AI) and Machine Learning (ML) stands as a transformative technology, poised to amplify the efficiency and precision of digital forensics investigations. However, the use of ML and AI in digital forensics is still in its nascent stages. As a result, this paper gives a thorough and in-depth analysis that goes beyond a simple survey and review. The goal is to look closely at how AI and ML techniques are used in digital forensics and incident response. This research explores cutting-edge research initiatives that cross domains such as data collection and recovery, the intricate reconstruction of cybercrime timelines, robust big data analysis, pattern recognition, safeguarding the chain of custody, and orchestrating responsive strategies to hacking incidents. This endeavour digs far beneath the surface to unearth the intricate ways AI-driven methodologies are shaping these crucial facets of digital forensics practice. While the promise of AI in digital forensics is evident, the challenges arising from increasing database sizes and evolving criminal tactics necessitate ongoing collaborative research and refinement within the digital forensics profession. This study examines the contributions, limitations, and gaps in the existing research, shedding light on the potential and limitations of AI and ML techniques. By exploring these different research areas, we highlight the critical need for strategic planning, continual research, and development to unlock AI's full potential in digital forensics and incident response. Ultimately, this paper underscores the significance of AI and ML integration in digital forensics, offering insights into their benefits, drawbacks, and broader implications for tackling modern cyber threats