Interpretable algorithmic forensics

Published: 2023 | By: Brandon Garrett

One of the most troubling trends in criminal investigations is the growing use of “black box” technology, in which law …

An Anti-Fuzzing Approach for Android Apps

Type: Conference Proceeding, Publication Research Area(s): Digital

Published: 2023 | By: Chris Chao-Chun Cheng

One of significant mobile app forensic analysis problems is the app evidence extraction from the device. Given the fact that …

Forensic Analysis of Android Cryptocurrency Wallet Applications

Type: Conference Proceeding, Publication Research Area(s): Digital

Published: 2023 | By: Chen Shi

Crypto wallet apps that integrate with various block-chains allow the users to make digital currencies transaction with QR codes. According …

Variations and Extensions of Information Leakage Metrics with Applications to Privacy Problems with Imperfect Statistical Information

Published: 2023 | By: Shahnewaz Karim Sakib

The conventional information leakage metrics assume that an adversary has complete knowledge of the distribution of the mechanism used to …

Camera Device Identification and the Effects of Underexposure

Published: 2023 | By: Seth Pierre

Technology today allows a photograph from a digital camera to be matched with the camera that took it. However, the …

Shifting decision thresholds can undermine the probative value and legal utility of forensic pattern-matching evidence

Published: 2023 | By: William Thompson

Forensic pattern analysis requires examiners to compare the patterns of items such as fingerprints or tool marks to assess whether …

A statistical approach to aid examiners in the forensic analysis of handwriting

Published: 2023 | By: Amy Crawford

We develop a statistical approach to model handwriting that accommodates all styles of writing (cursive, print, connected print). The goal …

Ensemble learning for score likelihood ratios under the common source problem

Type: Publication Research Area(s): Forensic Statistics

Published: 2023 | By: Federico Veneri

Machine learning-based score likelihood ratios (SLRs) have emerged as alternatives to traditional likelihood ratios and Bayes factors to quantify the …