Investigating visualisation techniques for rapid triage of digital forensic evidence

This study investigates the feasibility of a tool that allows digital forensics (DF) investigators to efficiently triage device datasets during the collection phase of an investigation. This tool utilises data visualisation techniques to display images found in near real-time to the end user. Findings indicate that participants were able to accurately identify contraband material whilst using this tool, however, classification accuracy dropped slightly with larger datasets. Combined with participant feedback, the results show that the proposed triage method is indeed feasible, and this tool provides a solid foundation for the continuation of further work

The Impact of Failing to Identify Suspect Effort in Patients Undergoing Adult Attention-Deficit/Hyperactivity Disorder (ADHD) Assessment

This retrospective study examines how many adult patients would plausibly receive a diagnosis of attention-deficit/hyperactivity disorder (ADHD) if performance and symptom validity measures were not administered during neuropsychological evaluations. Five hundred fifty-four patients were extracted from an archival clinical dataset. A total of 102 were diagnosed with ADHD based on cognitive testing, behavior rating scales, effort testing, and clinical interview; 115 were identified as putting forth suspect effort in accordance with the Slick, Sherman, and Iverson (1999) criteria. From a clinical decision-making perspective, suspect effort and ADHD groups were nearly indistinguishable on ADHD behavior, executive function, and functional impairment rating scales, as well as on cognitive testing and key clinical interview questions. These results suggest that a significant percentage of those making a suspect effort will be diagnosed with ADHD using the most commonly employed assessment methods: an interview alone (71%); an interview and ADHD behavior rating scales combined (65%); and an interview, behavior rating scales, and most continuous performance tests combined (57%). This research makes clear that it is essential to evaluate task engagement and possible symptom amplification during clinical evaluations

What science can teach us about “Enhanced Interrogation”

No abstract available

Truth-tellers stand the test of time and contradict evidence less than liars, even months after a crime

When deceptive suspects are unaware of the evidence the police hold against them, they contradict that evidence more than truthful suspects do – a useful cue to deception. But given that, over time, truthful suspects might forget the past and also contradict the evidence, how effective are lie detection techniques that rely on such inconsistencies when suspects are questioned months after a crime? In Experiment 1, people committed a theft (liars) or a benign activity (truth-tellers) in a university bookshop. Shortly after or two months later, we questioned them about their bookshop visit without informing them of the evidence implicating them in the theft. Though truth-tellers contradicted some evidence after both time delays, liars always contradicted the evidence more than did truth-tellers. In Experiment 2, we presented the mock suspects’ responses to an independent group of laypeople and asked them to rate how deceptive the suspects were. Laypeople rated liars as more deceptive than truth-tellers after both time delays, but also rated truth-tellers questioned two months after the crime as more deceptive than truth-tellers questioned shortly after the crime. These findings suggest that liars’ tendency to distance themselves from a crime might outweigh any memory decay that truth-tellers experience in the two months following a crime. As a result, the extent of a suspect’s contradictions with the evidence could still be diagnostic of deception even after an extended time delay

Do GANs leave artificial fingerprints?

In the last few years, generative adversarial networks (GAN) have shown tremendous potential for a number of applications in computer vision and related fields. With the current pace of progress, it is a sure bet they will soon be able to generate high-quality images and videos, virtually indistinguishable from real ones. Unfortunately, realistic GAN-generated images pose serious threats to security, to begin with a possible flood of fake multimedia, and multimedia forensic countermeasures are in urgent need. In this work, we show that each GAN leaves its specific fingerprint in the images it generates, just like real-world cameras mark acquired images with traces of their photo-response non-uniformity pattern. Source identification experiments with several popular GANs show such fingerprints to represent a precious asset for forensic analyses