Welcome to the new-look MARS. See something that needs attention? Use our "Send Feedback" link at page bottom.
 

Adversarial Face Recognition and Phishing Detection Using Multi-Layer Data Fusion

Date

2012

Authors

Ramanathan, Venkatesh

Journal Title

Journal ISSN

Volume Title

Publisher

Abstract

This thesis addresses digital identity for biometric / face recognition screening and cyberspace security subject to denial and deception characteristic of adversarial behavior. The adversarial aspect concerns defense and offense operations that involve impostors and identity theft. Denial and deception correspond to occlusion and disguise for biometrics, while for cyberspace security they correspond to spoofing and obfuscation. To prevent or mitigate the impacts of adversarial behavior from offensive attacks this thesis proposes the use of multi-layer data fusion. Multi-layer aspect of fusion refers to features, representations, algorithms, decision-making, adversarial aspects and their purposeful combinations. This novelty, feasibility, and utility of our research is illustrated in the physical and cyber worlds: (i) robust face recognition in the presence of occlusion and disguise, and (ii) phishing detection to prevent identity theft through spoofing and obfuscation.

Description

Keywords

Computer science, Conditional Random Field, Correlation Filters, Face Recognition, Latent Dirichlet Allocation, Named Entities, Phishing

Citation