Novel Similarity Measurements for Reassembling Fragmented Image Files

Chapter 6 - Digital Forensics at Work in the Legal System

Latent fingerprints are among the most important and widely used evidence in crime scenes, digital forensics and law enforcement worldwide. Despite the number of advancements reported in recent works, we note that significant open issues such as independent benchmarking and lack of large-scale evaluation databases for improving the algorithms are inadequately addressed. The available databases are mostly of semi-public nature, lack of acquisition in the wild environment, and post-processing pipelines. Moreover, they do not represent a realistic capture scenario similar to real crime scenes, to benchmark the robustness of the algorithms. Further, existing databases for latent fingerprint recognition do not have a large number of unique subjects/fingerprint instances or do not provide ground truth/reference fingerprint images to conduct a cross-comparison against the latent. In this paper, we introduce a new wild large-scale latent fingerprint database that includes five different acquisition scenarios: reference fingerprints from (1) optical and (2) capacitive sensors, (3) smartphone fingerprints, latent fingerprints captured from (4) wall surface, (5) Ipad surface, and (6) aluminium foil surface. The new database consists of 1,318 unique fingerprint instances captured in all above mentioned settings. A total of 2,636 reference fingerprints from optical and capacitive sensors, 1,318 fingerphotos from smartphones, and 9,224 latent fingerprints from each of the 132 subjects were provided in this work. The dataset is constructed considering various age groups, equal representations of genders and backgrounds. In addition, we provide an extensive set of analysis of various subset evaluations to highlight open challenges for future directions in latent fingerprint recognition research.

The development of sixth‐generation (6G) wireless communication technology is expected to provide super high‐speed data transmission, and advanced network performance than the current fifth‐generation (5G) and be fully functional by the 2030s. This development will have a significant impact and add improvements to digital extended reality (XR), autonomous systems, vehicular ad hoc networks (VANETs), artificial intelligence (AI), underwater communications, blockchain technology, pervasive biomedical informatics and smart cities built on the digital infrastructure backbone of the Internet of Things (IoT). The ubiquitous nature of this large‐scale 6G‐enabled IoT that offers faster connectivity capabilities and integrates both terrestrial and non‐terrestrial networks will not only create new data security and privacy issues but also provide a treasure trove of digital evidence useful for digital forensic examiners investigating security incidents and cybercrime. However, for digital forensic examiners, evidence collection, preservation and analysis will become a priority in the successful deployment of 6G IoT networks. In this study, we define key applications of 6G network technology to the Internet of Things and its existing architectures. The survey introduces potential digital forensic challenges and related issues affecting digital forensic investigations specific to 6G IoT networks. Finally, we highlight and discuss forensic readiness and future research directions for identified challenges within the 6G IoT network environments.This article is categorized under: Digital and Multimedia Science > IoT Forensics

The paper expands on the role of scientific evidences within criminal justice. After a brief analysis on Deoxyribonucleic acid (DNA) and ballistics evidence, it focuses on the issues connected to the use of digital forensics evidence.

This paper reviews the existing methodologies and best practices for digital investigations phases like collecting, evaluating and preserving digital forensic evidence and chain of custody of cybercrimes. Cybercriminals are adopting new strategies to launch cyberattacks within modified and ever changing digital ecosystems, this article proposes that digital investigations must continually readapt to tackle cybercrimes and prosecute cybercriminals, working in international collaboration networks, sharing prevention knowledge and lessons learned. The authors also introduce a compact cyber forensics model for diverse technological ecosystems called Cyber Forensics Model in Digital Ecosystems (CFMDE). Transferring the knowledge, international collaboration, best practices and adopting new digital forensic tools, methodologies and techniques will be hereinafter paramount to obtain digital evidence, enforce organizational cybersecurity policies, mitigate security threats, fight anti-forensics practices and indict cybercriminals. The global Digital Forensics community ought to constantly update current practices to deal with cybercriminality and foreseeing how to prepare to new technological environments where change is always constant.

In the case of digital forensic investigations, the potential digital evidence captured, the analysis, interpretation, and attribution must ultimately be presented in the form of expert reports, depositions, and testimony in any legal proceedings. If the presentation and interpretation of the potential digital evidence is conducted correctly, it is much easier and useful in apprehending the attacker and stands a much greater chance of being admissible in the event of a prosecution. Wrongly presented and interpreted potential digital evidence data might create loopholes for perpetrators to exploit, thus, making it hard to convict and prosecute them. Existing digital forensic investigation process models have provided guidelines for identifying and preserving potential digital evidence captured from a crime scene. However, the extent to which such potential digital evidence may be admissible in a court of law remains a challenge to investigators. This is backed up by the fact that there are currently no standardised guidelines for even presenting the most common representations of digital forensic evidence. Therefore, in the authors' opinion, methodologies and specifications need to be developed in the field of digital forensics with the ability to effectively enhance the potential digital evidence presentation and interpretation in any legal proceedings. In this paper, therefore, we present a step-by-step framework in an attempt to propose high-level guidelines for enhancing the potential digital evidence presentation in any legal proceedings. Such a framework will be helpful to digital forensic experts, for example, in structuring investigation findings as well as in identifying relevant patterns of events to be incorporated during the presentation of potential digital evidence. The framework will also assist law enforcement agencies, for example, to determine, with less effort, the validity, weight and admissibility of any potential digital evidence presented. However, it should be noted that the purpose of this paper is not to replace any of the extensive and known evidence presentation principles, but serves as a survey of the state of the art of the research area while proposing harmonised and high-level guidelines for enhancing the presentation of potential digital evidence in legal proceedings.

Cloud Storage Forensics

BCFL logging: An approach to acquire and preserve admissible digital forensics evidence in cloud ecosystem

Repeatability of aspects of genotype by environment (GxE) interactions is an important factor to be assessed in designing more efficient selection programmes. Sugar yield data from multi environment trials (METs) which were part of the sugarcane breeding programme in southern Queensland were analysed. Data were obtained from 71 environments consisting of trials planted from 1986 to 1989. Retrospective analysis on these data was conducted to assess the repeatability of the clone by environment (CxE) interactions over locations and years. This analysis focussed on identifying similarities among test environments in the way they discriminated among clones for sugar yield. Analyses of variance and pattern analyses on environments over years based on standardised data were conducted. The pattern analyses were done sequentially according to the accumulated data sets over years. Squared Euclidean distances among environments were averaged over data sets and years before pattern analyses across the data sets were conducted. A graphical methodology was developed to present the results of the cumulative historical analysis. CxE interactions of a magnitude which affected selection decisions were present in each data set studied. Pattern analyses on cumulative data sets identified environmental groupings that were based on geographical positions. Each location generated a different pattern of discrimination among the clones. These results emphasised the importance of clone by location (CxL) interactions in southern Queensland and the need to concentrate more on testing across locations than on ratooning ability within a location. The classifications identified similarities among ratoon crops within a location, differences among locations and differences between ratoon crops and their plant crop (PC). This suggested that some aspects of CxL and clone by crop-year (CxY) interactions were repeatable across years. The potential applications of these results to increase efficiency of the sugarcane breeding programme, such as the possibility of applying indirect selection among environments generating similar discrimination among clones, are discussed.

Introduction. Rack stacker cranes are the main hoisting and transport machine for maintenance of multi-level rack warehouses. The efficiency and profitability of the warehouse depends on their efficiency. The traditional design of a rack stacker crane includes a trolley that moves along the rails on the floor of a warehouse, a column mounted vertically on the trolley, along which a carriage with a load gripping device and a load rises. The disadvantages of the traditional crane design are the relatively large mass of the trolley, the need to move it along with the column in a horizontal direction when moving around the warehouse, the relatively high energy consumption and the need to strengthen the floor of the warehouse. The crane rail tracks on the floor of the warehouse must be cleared of objects falling from above, which is associated with stopping the operation of the crane and reducing productivity.Materials and Methods. The design of a stacker crane is related to the shape of its working space. In order to improve the stacker crane, its design was developed on the basis of a jib with a counterweight, which does not require rail crane tracks. The crane of the proposed design is capable of storage in the hangars of a semicircular shape. For two warehouses: traditional rectangular and semicircular shapes, a comparative analysis of the sums of geometric distances was performed when the load gripping device moved from the loading point to randomly selected target cells. As a comparison criterion, the sum of the Cartesian distances of the load gripping device movements between the warehouse loading point and the target points, randomly selected according to the law of uniform distribution, was used. Three types of crane operation cycles were studied – simple single, double and mixed.Results. Comparative diagrams of criterion values are given. It has been established that for all types of cycles studied, to the greatest extent when working on a single cycle, the use of a crane of the proposed design with the same storage capacity significantly reduces the total geometric distance that the load gripping device must pass.Discussion and conclusions. Reducing the sum of the geometric distances of movement of the load handling device of the new crane allows to conclude that the time spent on the movements that determine the productivity of the work performed by the crane, as well as the energy costs associated with them, can be significantly reduced. Reducing energy costs is possible by replacing the translational movement of a massive undercarriage with the rotational movement of a balanced jib of a relatively small mass at the crane of the proposed design. Time is not wasted on regular cleaning of the crane tracks, associated with stopping the crane. The number of friction pairs in the proposed design of the crane is reduced compared with the traditional one.

Approximation schemes for Min-Sum [formula omitted]-Clustering

Content Based Image Retrieval (CBIR) is a process in which for a given query image similar images will be retrieved based on the image content similarity. Image content refers to its visual features, which are mathematical representations of a digital image. The image retrieval task primarily depends on image feature extraction and similarity measurement between the feature vectors. The performance of CBIR process not only decided by the optimum features extracted from the image but also on the proper choice of the similarity/dissimilarity measures (distance metrics). As the image features broadly diversified in terms of color, texture and shape based, using the same distance metric may not work well for all of these features. In this paper, first we presented overview of geometric and statistical distance metrics used in CBIR along with the comparative analysis of these measures on color and texture features. Color features extracted by computing color histograms in HSV space and texture features by wavelet decompositions. Geometrical distances such as Manhattan, Chebyshev, Euclidean, statistical distance metrics such as Cosine Similarity, Chi-square, KullbackLeibler, Jeffrey and cumulative statistical distance metrics of Kolmogorov-Smirnov, Cramer von Mises and Earthmover's distances were analyzed for feature similarity. We gave certain conclusions on the performance of all these distance metrics in terms of Mean Average Precision (MAP) and Recall rates with color and texture features respectively.

Artificial Intelligence (AI) is transforming forensic investigations, enhancing accuracy, speed, and efficiency in crime-solving. This paper explores how AI is revolutionizing forensic practices by automating tasks, improving data analysis, and providing predictive insights. It highlights key applications, such as facial recognition, pattern analysis, and digital forensics, which reduce manual effort and minimize human error. The paper also examines challenges such as ethical concerns, data privacy, and technical limitations, proposing solutions to address these issues. As AI continues to evolve, its role in enabling rapid and accurate evidence processing becomes increasingly critical. This study underscores the necessity of ethical frameworks and collaborative efforts to ensure responsible AI integration. By leveraging AI's transformative capabilities, forensic science can bolster justice systems and enhance public safety in an era of growing technological complexities. The findings and recommendations in this paper aim to provide a roadmap for harnessing AI's potential while addressing the challenges it presents.

As the volume of potential digital evidence increases, digital forensic practitioners are challenged to determine the best allocation of their limited resources. While automation will continue to partially mitigate this problem, the preliminary question about which media should be examined by human or machine remains largely unsolved. This chapter describes and validates a methodology for assessing digital media similarity to assist with digital media triage decisions. The application of the methodology is predicated on the idea that unexamined media is likely to be relevant or interesting to a practitioner if the media is similar to other media that were previously determined to be relevant or interesting. The methodology builds on prior work using sector hashing and the Jaccard index of similarity. These two methods are combined in a novel manner and the accuracy of the resulting methodology is demonstrated using a collection of hard drive images with known ground truth. The work goes beyond interesting file and file fragment matching. Specifically, it assesses the overall similarity of digital media to identify systems that might share applications and thus be related, even if common files of interest are encrypted, deleted or otherwise unavailable. In addition to triage decisions, digital media similarity may be used to infer links and associations between disparate entities.

Multivariate pattern analyses are widely used in neuroimaging studies. But it is unclear what these analyses reveal about neural coding. Such approaches often obfuscate the link between neural measurements and the stimulus, greatly limiting their utility. Linear classification, a highly-popular approach, exploits the Euclidean distance among pattern vectors. Our recent theoretical work suggests that information regarding tuning properties is best captured by angular distances, not Euclidean distances. The goal of this experiment was to relate specific aspects of multivariate measurements to parametrically defined dimensions of visual stimuli. We used primary visual cortex as a model system to test the following predictions: (i) Euclidean distances conflate information regarding response amplitude and selectivity, (ii) angular distances best reflect information about selectivity, and (iii) data demeaning—a common preprocessing procedure—invalidates inferences regarding selectivity because it changes angular relationships among pattern vectors. fMRI BOLD activity was measured from 5 participants (7T, 32-channel coil, 1.2x1.2x1.2 mm resolution). Stimuli consisted of Cartesian sinusoidal gratings multiplied by flower-shaped apertures. The orientation of the “petals” was controlled by the phase of the radial frequency (RF) used to generate the aperture. In a fully crossed experimental design, the compound stimulus varied along three dimensions: grating orientation, contrast, and RF-phase, resulting in a total of 96 unique conditions. We found that the Euclidean metric was sensitive to stimulus contrast and relatively insensitive to grating orientation and aperture phase. Angular distances proved sensitive to grating orientation and aperture phase, and robust to changes in stimulus contrast. As predicted, data demeaning led to mischaracterizations of neural tuning. Finally, we used a biologically-inspired image-computable model to provide a theoretic account of these observations. Our results have fundamental implications for the interpretation of MVPA, and inform the development of data analysis strategies that are more readily interpretable in terms of the underlying neurophysiology.