Computational criminology is an interdisciplinary field which uses computing science methods to formally define criminology concepts, improve our understanding of complex phenomena, and generate solutions for related problems.
Computing science methods being used include:
Computational criminology is interdisciplinary in the sense that both criminologists and computing scientists work together to ensure that computational models properly match their theoretical and real-world counterparts. Areas of criminology for which computational approaches are being used include:
Computational forensics (CF) is a quantitative approach to the methodology of the forensic sciences. It involves computer-based modeling, computer simulation, analysis, and recognition in studying and solving problems posed in various forensic disciplines. CF integrates expertise from computational science and forensic sciences.
A broad range of objects, substances and processes are investigated, which are mainly based on pattern evidence, such as toolmarks, fingerprints, shoeprints, documents etc., [1] but also physiological and behavioral patterns, DNA, digital evidence and crime scenes.
Computational methods find a place in the forensic sciences in several ways, [2] [3] [4] [5] [6] as for example:
Algorithms implemented are from the fields of signal and image processing, computer vision, [7] computer graphics, data visualization, statistical pattern recognition, data mining, machine learning, and robotics.
Computer forensics (also referred to as "digital forensics" or "forensic information technology") is one specific discipline that could use computational science to study digital evidence. Computational Forensics examines diverse types of evidence.
Forensic animation is a branch of forensic science in which audio-visual reconstructions of incidents or accidents are created to aid investigators. Examples include the use of computer animation, stills, and other audio visual aids. Application of computer animation in courtrooms today is becoming more popular.
The first use of forensic animation was in Connors v. United States, both sides used computer re-creations and animations in a case surrounding the crash of Delta Flight 191 on August 2, 1985. [8] The crash resulted in the deaths of 137 people and extensive property damage. In the resulting lawsuit a method was required to explain complicated information and situations to the jury. As part of the plaintiff presentation, a 45-minute computer generated presentation was created to explain the intricacies of the evidence and thus began forensic animation. [9]
The first reported use of computer animation in a U.S. criminal trial was in the 1991 Marin County, CA homicide trial of James Mitchell (of the porno-businessman Mitchell Brothers) [10] The prosecution used the animation to explain the complex details of the shooting incident to the jury. It showed the positions of James Mitchell, Artie Mitchell (the victim), the bullet impact points, and the path taken by bullets as they entered Artie's body. The animation was admitted, over objection by the defense, and the case resulted in a conviction. The use of the animation was upheld on appeal and the success of the forensic animation led to its use in many other trials. In India Prof. T D Dogra at AIIMS New Delhi in 2008 used animation to explain the court of law and investigating agencies first time in two important cases of firearm injuries, case of Murder and Terrorist encounter killings (Batla house encounter case). [11]
Image registration is the process of transforming different sets of data into one coordinate system. Data may be multiple photographs, data from different sensors, times, depths, or viewpoints. It is used in computer vision, medical imaging, military automatic target recognition, and compiling and analyzing images and data from satellites. Registration is necessary in order to be able to compare or integrate the data obtained from these different measurements.
In computer science, evolutionary computation is a family of algorithms for global optimization inspired by biological evolution, and the subfield of artificial intelligence and soft computing studying these algorithms. In technical terms, they are a family of population-based trial and error problem solvers with a metaheuristic or stochastic optimization character.
Neuromorphic computing is an approach to computing that is inspired by the structure and function of the human brain. A neuromorphic computer/chip is any device that uses physical artificial neurons to do computations. In recent times, the term neuromorphic has been used to describe analog, digital, mixed-mode analog/digital VLSI, and software systems that implement models of neural systems. The implementation of neuromorphic computing on the hardware level can be realized by oxide-based memristors, spintronic memories, threshold switches, transistors, among others. Training software-based neuromorphic systems of spiking neural networks can be achieved using error backpropagation, e.g., using Python based frameworks such as snnTorch, or using canonical learning rules from the biological learning literature, e.g., using BindsNet.
Computer forensics is a branch of digital forensic science pertaining to evidence found in computers and digital storage media. The goal of computer forensics is to examine digital media in a forensically sound manner with the aim of identifying, preserving, recovering, analyzing and presenting facts and opinions about the digital information.
The expression computational intelligence (CI) usually refers to the ability of a computer to learn a specific task from data or experimental observation. Even though it is commonly considered a synonym of soft computing, there is still no commonly accepted definition of computational intelligence.
A neural network can refer to either a neural circuit of biological neurons, or a network of artificial neurons or nodes in the case of an artificial neural network. Artificial neural networks are used for solving artificial intelligence (AI) problems; they model connections of biological neurons as weights between nodes. A positive weight reflects an excitatory connection, while negative values mean inhibitory connections. All inputs are modified by a weight and summed. This activity is referred to as a linear combination. Finally, an activation function controls the amplitude of the output. For example, an acceptable range of output is usually between 0 and 1, or it could be −1 and 1.
A demosaicing algorithm is a digital image process used to reconstruct a full color image from the incomplete color samples output from an image sensor overlaid with a color filter array (CFA). It is also known as CFA interpolation or color reconstruction.
Honeywell, Inc. v. Sperry Rand Corp., et al., 180 U.S.P.Q. 673, was a landmark U.S. federal court case that in October 1973 invalidated the 1964 patent for the ENIAC, the world's first general-purpose electronic digital computer. The decision held, in part, the following: 1. that the ENIAC inventors had derived the subject matter of the electronic digital computer from the Atanasoff–Berry computer (ABC), prototyped in 1939 by John Atanasoff and Clifford Berry, 2. that Atanasoff should have legal recognition as the inventor of the first electronic digital computer and 3. that the invention of the electronic digital computer ought to be placed in the public domain.
Les Hatton is a British-born computer scientist and mathematician most notable for his work on failures and vulnerabilities in software controlled systems.
The following outline is provided as an overview of and topical guide to forensic science:
Spiking neural networks (SNNs) are artificial neural networks that more closely mimic natural neural networks. In addition to neuronal and synaptic state, SNNs incorporate the concept of time into their operating model. The idea is that neurons in the SNN do not transmit information at each propagation cycle, but rather transmit information only when a membrane potential – an intrinsic quality of the neuron related to its membrane electrical charge – reaches a specific value, called the threshold. When the membrane potential reaches the threshold, the neuron fires, and generates a signal that travels to other neurons which, in turn, increase or decrease their potentials in response to this signal. A neuron model that fires at the moment of threshold crossing is also called a spiking neuron model.
Informatics is the study of computational systems. According to the ACM Europe Council and Informatics Europe, informatics is synonymous with computer science and computing as a profession, in which the central notion is transformation of information. In other countries, the term "informatics" is used with a different meaning in the context of library science, in which case it is synonymous with data storage and retrieval.
Sargur Narasimhamurthy Srihari was an Indian and American computer scientist and educator who made contributions to the field of pattern recognition. The principal impact of his work has been in handwritten address reading systems and in computer forensics. He was a SUNY Distinguished Professor in the School of Engineering and Applied Sciences at the University at Buffalo, Buffalo, New York, USA.
Audio forensics is the field of forensic science relating to the acquisition, analysis, and evaluation of sound recordings that may ultimately be presented as admissible evidence in a court of law or some other official venue.
Bashir Mohammed Ali Al-Hashimi, CBE, FRS, FREng, FIEEE, FIET, FBCS is a recognised multidisciplinary global researcher with sustained and pioneering contributions to computer engineering and a prominent academic and higher education leader. He is Vice President and ARM Professor of Computer Engineering at King's College London in the United Kingdom. He was the co-founder and co-director of the ARM-ECS Research Centre, an industry-university collaboration partnership involving the University of Southampton and ARM. He is actively involved in promoting science and engineering for young people and regularly contributes to engineering higher education and skills national debates.
Dynamic texture is the texture with motion which can be found in videos of sea-waves, fire, smoke, wavy trees, etc. Dynamic texture has a spatially repetitive pattern with time-varying visual pattern. Modeling and analyzing dynamic texture is a topic of images processing and pattern recognition in computer vision.
Ali Dehghantanha is an academic-entrepreneur in cybersecurity and cyber threat intelligence. He is a Professor of Cybersecurity and a Canada Research Chair in Cybersecurity and Threat Intelligence.
IoT Forensics is a branch of Digital forensics that has the goal of identifying and extracting digital information from devices belonging to the Internet of things field, using a forensically sound and legally acceptable process.