A common question in forensic analysis is whether two observed data sets originated from the same source or from different sources. Statistical approaches to addressing this question have been widely
An Exploratory Analysis of Handwriting Features: Investigating Numeric Measurements of Writing That Are Important for Statistical Modeling
The goal of this presentation is to provide insights into which features of handwritten documents are important for statistical modeling with the task of writer identification and to discuss how
The goals of this workshop are to: (1) introduce attendees to the basics of supervised learning algorithms in the context of forensic applications, including firearms and footwear examination and trace
The goal of this presentation is to provide insights into features of handwritten documents that are important for statistical modeling with the task of writer identification.
The learning objectives of this presentation include the following: Introduce an objective method to quantify the similarity between two outsole impressions, show that this algorithm is accurate and reliable even
After attending this presentation, attendees will be familiar with the ways that CNNs can be applied to classify forensic pattern evidence, specifically with shoe outsole features.
After attending this presentation, attendees will better understand how AndroidAED will be beneficial for academic researchers whose studies relate to mobile applications that grant them the ability to search through
As mobile Internet and telecommunication technology develops at high speed, the digital image forensics academic community is facing a growing challenge. • Mobile applications (Apps) allow a user to easily
Many areas of forensics are moving away from the notion of classifying evidence simply as a match or non-match. Instead, some use score-based likelihood ratios (SLR) to quantify the similarity
The number and availability of stegonographic embedding algorithms continues to grow. Many traditional blind steganalysis frameworks require training examples from every embedding algorithm, but collecting, storing and processing representative examples