5,203 research outputs found
Forensic Face Recognition: A Survey
Beside a few papers which focus on the forensic aspects of automatic face recognition, there is not much published about it in contrast to the literature on developing new techniques and methodologies for biometric face recognition. In this report, we review forensic facial identification which is the forensic expertsâ way of manual facial comparison. Then we review famous works in the domain of forensic face recognition. Some of these papers describe general trends in forensics [1], guidelines for manual forensic facial comparison and training of face examiners who will be required to verify the outcome of automatic forensic face recognition system [2]. Some proposes theoretical framework for application of face recognition technology in forensics [3] and automatic forensic facial comparison [4, 5]. Bayesian framework is discussed in detail and it is elaborated how it can be adapted to forensic face recognition. Several issues related with court admissibility and reliability of system are also discussed. \ud
Until now, there is no operational system available which automatically compare image of a suspect with mugshot database and provide result usable in court. The fact that biometric face recognition can in most cases be used for forensic purpose is true but the issues related to integration of technology with legal system of court still remain to be solved. There is a great need for research which is multi-disciplinary in nature and which will integrate the face recognition technology with existing legal systems. In this report we present a review of the existing literature in this domain and discuss various aspects and requirements for forensic face recognition systems particularly focusing on Bayesian framework
Medical Cyber-Physical Systems Development: A Forensics-Driven Approach
The synthesis of technology and the medical industry has partly contributed
to the increasing interest in Medical Cyber-Physical Systems (MCPS). While
these systems provide benefits to patients and professionals, they also
introduce new attack vectors for malicious actors (e.g. financially-and/or
criminally-motivated actors). A successful breach involving a MCPS can impact
patient data and system availability. The complexity and operating requirements
of a MCPS complicates digital investigations. Coupling this information with
the potentially vast amounts of information that a MCPS produces and/or has
access to is generating discussions on, not only, how to compromise these
systems but, more importantly, how to investigate these systems. The paper
proposes the integration of forensics principles and concepts into the design
and development of a MCPS to strengthen an organization's investigative
posture. The framework sets the foundation for future research in the
refinement of specific solutions for MCPS investigations.Comment: This is the pre-print version of a paper presented at the 2nd
International Workshop on Security, Privacy, and Trustworthiness in Medical
Cyber-Physical Systems (MedSPT 2017
Towards a Bayesian evaluation of features in questioned handwritten signatures
In this work, we propose the construction of a evaluative framework for supporting experts in questioned signature examinations. Through the use of Bayesian networks, we envision to quantify the probative value of well defined measurements performed on questioned signatures, in a way that is both formalised and part of a coherent approach to evaluation.
At the current stage, our project is explorative, focusing on the broad range of aspects that relate to comparative signature examinations. The goal is to identify writing features which are both highly discriminant, and easy for forensic examiners to detect. We also seek for a balance between case-specific features and characteristics which can be measured in the vast majority of signatures. Care is also taken at preserving the interpretability at every step of the reasoning process.
This paves the way for future work, which will aim at merging the different contributions to a single probabilistic measure of strength of evidence using Bayesian networks
Forensics Writer Identification using Text Mining and Machine Learning
Constant technological growth has resulted in the danger and seriousness of cyber-attacks, which has recently unmistakably developed in various institutions that have complex Information Technology (IT) infrastructure. For instance, for the last three (3) years, the most horrendous instances of cybercrimes were perceived globally with enormous information breaks, fake news spreading, cyberbullying, crypto-jacking, and cloud computing services. To this end, various agencies improvised techniques to curb this vice and bring perpetrators, both real and perceived, to book in relation to such serious cybersecurity issues. Consequently, Forensic Writer Identification was introduced as one of the most effective remedies to the concerned issue through a stylometry application. Indeed, the Forensic Writer Identification is a complex forensic science technology that utilizes Artificial Intelligence (AI) technology to safeguard, recognize proof, extraction, and documentation of the computer or digital explicit proof that can be utilized by the official courtroom, especially, the investigative officers in case of a criminal issue or just for data analytics. This research\u27s fundamental objective was to scrutinize Forensic Writer Identification technology aspects in twitter authorship analytics of various users globally and apply it to reduce the time to find criminals by providing the Police with the most accurate methodology. As well as compare the accuracy of different techniques.
The report shall analytically follow a logical literature review that observes the vital text analysis techniques. Additionally, the research applied agile text mining methodology to extract and
analyze various Twitter users\u27 texts. In essence, digital exploration for appropriate academics and scholarly artifacts was affected in various online and offline databases to expedite this research. Forensic Writer Identification for text extraction, analytics have recently appreciated reestablished attention, with extremely encouraging outcomes. In fact, this research presents an overall foundation and reason for text and author identification techniques. Scope of current techniques and applications are given, additionally tending to the issue of execution assessment. Results on various strategies are summed up, and a more inside and out illustration of two consolidated methodologies are introduced. By encompassing textural, algorithms, and allographic, emerging technologies are beginning to show valuable execution levels. Nevertheless, user acknowledgment would play a vital role with regards to the future of technology. To this end, the goal of coming up with a project proposal was to come up with an analytical system that would automate the process of authorship identification methodology in various Web 2.0 Technologies aspects globally, hence addressing the contemporary cybercrime issues
Bayesian Non-Exhaustive Classification A Case Study: Online Name Disambiguation using Temporal Record Streams
The name entity disambiguation task aims to partition the records of multiple
real-life persons so that each partition contains records pertaining to a
unique person. Most of the existing solutions for this task operate in a batch
mode, where all records to be disambiguated are initially available to the
algorithm. However, more realistic settings require that the name
disambiguation task be performed in an online fashion, in addition to, being
able to identify records of new ambiguous entities having no preexisting
records. In this work, we propose a Bayesian non-exhaustive classification
framework for solving online name disambiguation task. Our proposed method uses
a Dirichlet process prior with a Normal * Normal * Inverse Wishart data model
which enables identification of new ambiguous entities who have no records in
the training data. For online classification, we use one sweep Gibbs sampler
which is very efficient and effective. As a case study we consider
bibliographic data in a temporal stream format and disambiguate authors by
partitioning their papers into homogeneous groups. Our experimental results
demonstrate that the proposed method is better than existing methods for
performing online name disambiguation task.Comment: to appear in CIKM 201
Exact Inference Techniques for the Analysis of Bayesian Attack Graphs
Attack graphs are a powerful tool for security risk assessment by analysing
network vulnerabilities and the paths attackers can use to compromise network
resources. The uncertainty about the attacker's behaviour makes Bayesian
networks suitable to model attack graphs to perform static and dynamic
analysis. Previous approaches have focused on the formalization of attack
graphs into a Bayesian model rather than proposing mechanisms for their
analysis. In this paper we propose to use efficient algorithms to make exact
inference in Bayesian attack graphs, enabling the static and dynamic network
risk assessments. To support the validity of our approach we have performed an
extensive experimental evaluation on synthetic Bayesian attack graphs with
different topologies, showing the computational advantages in terms of time and
memory use of the proposed techniques when compared to existing approaches.Comment: 14 pages, 15 figure
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