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




Ramanathan, Venkatesh

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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.



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