Towards similarity learning in security applications

Hao, Qingying

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https://hdl.handle.net/2142/130183 Copy

Description

Title

Towards similarity learning in security applications

Author(s)

Hao, Qingying

Issue Date

2025-07-15

Director of Research (if dissertation) or Advisor (if thesis)

Wang, Gang

Doctoral Committee Chair(s)

Wang, Gang

Committee Member(s)

• Gunter, Carl
• Li, Bo
• Chandrasekaran, Varun
• Conti, Mauro

Department of Study

Siebel School Comp & Data Sci

Discipline

Computer Science

Degree Granting Institution

University of Illinois Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

• Security
• Machine Learning
• Similarity Learning

Language

eng

Abstract

In today’s world, large amounts of data remain unlabeled, posing a major challenge, especially in security applications, where acquiring high-quality labels is costly and difficult. Without accurate labels, it is hard to train reliable machine learning (ML) models, which limits their effectiveness in real-world scenarios. Similarity learning provides a promising direction by capturing relationships within the data without requiring explicit labels. Instead, it learns from reference pairs by measuring similarity through distance. While simple distance metrics can be used , similarity learning is often combined with deep learning to learn robust feature representations for comparison using predefined or learned similarity measures. How reliable is similarity learning in real-world security applications, particularly when exposed to adversarial threats? Under what conditions can it enhance model generalization and detection performance? This dissertation evaluates the robustness of similarity-learning applications under a realistic threat model by applying adversarial attacks end-to-end, and shows how similarity learning can improve out-of-distribution (OOD) generalization in graph-structured data. Specifically, Chapter 3 presents adversarial attacks targeting perceptual hashing-based reverse image search engines, which use Hamming distance as the similarity metric. By developing advanced attacks and evaluating them end-to-end on real-world systems, our framework successfully subverts several major reverse image search engines. In Chapter 4, we present attacks on vision-based phishing detectors trained using similarity learning. Our framework generates adversarial logos that preserve original brand semantics while bypassing state-of-the-art visual phishing website detectors. Chapter 5 explores how similarity learning, specifically graph contrastive learning (GCL), can complement supervised learning to improve out-of-distribution generalization in graph neural networks (GNNs) under natural distribution shifts. In summary, these studies show that similarity learning-based applications are vulnerable to adversarial attacks, highlighting the need for stronger defenses under realistic end-to-end threat models. At the same time, similarity learning can complement supervised methods by providing diverse feature representations and decision signals, making it valuable for improving out-of-distribution detection under natural distribution shifts.

Graduation Semester

2025-08

Type of Resource

Text

Handle URL

https://hdl.handle.net/2142/130183

Copyright and License Information

Copyright 2025 Qingying Hao

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