197 citations · 299 across the 47 of their papers we have counts for
9 papers · 1 filter
Automatically Detecting Online Deceptive Patterns
Asmit Nayak, Shirley Zhang, Yash Wani +2
Deceptive patterns in digital interfaces manipulate users into making unintended decisions, exploiting cognitive biases and psychological vulnerabilities. These patterns have becom…
Reliable Heading Tracking for Pedestrian Road Crossing Prediction Using Commodity Devices
Yucheng Yang, Jingjie Li, Kassem Fawaz
Pedestrian heading tracking enables applications in pedestrian navigation, traffic safety, and accessibility. Previous works, using inertial sensor fusion or machine learning, are…
Evaluating the Impact of Warning Modalities and False Alarms in Pedestrian Crossing Alert System
Hesham Alyamani, Yucheng Yang, David Noyce +2
With the steadily increasing pedestrian fatalities, pedestrian safety is a growing concern, especially in urban environments. Advanced Driver Assistance Systems (ADAS) have been de…
PolicyLR: A Logic Representation For Privacy Policies
Ashish Hooda, Rishabh Khandelwal, Prasad Chalasani +2
Privacy policies are crucial in the online ecosystem, defining how services handle user data and adhere to regulations such as GDPR and CCPA. However, their complexity and frequent…
CounterFace: A Synthetic Face Dataset for Fine-Grained Counterfactual Evaluation of Face Recognition Systems
Guruprasad Viswanathan Ramesh, Ashish Hooda, Shimaa Ahmed +3
Face recognition (FR) systems are widely deployed in critical applications, making their reliability and robustness across diverse populations and conditions essential. Standard ev…
A Picture is Worth 500 Labels: A Case Study of Demographic Disparities in Local Machine Learning Models for Instagram and TikTok
Jack West, Lea Thiemt, Shimaa Ahmed +3
Mobile apps have embraced user privacy by moving their data processing to the user's smartphone. Advanced machine learning (ML) models, such as vision models, can now locally analy…
PRP: Propagating Universal Perturbations to Attack Large Language Model Guard-Rails
Neal Mangaokar, Ashish Hooda, Jihye Choi +4
Large language models (LLMs) are typically aligned to be harmless to humans. Unfortunately, recent work has shown that such models are susceptible to automated jailbreak attacks th…
I see an IC: A Mixed-Methods Approach to Study Human Problem-Solving Processes in Hardware Reverse Engineering
René Walendy, Markus Weber, Jingjie Li +7
Trust in digital systems depends on secure hardware, often assured through Hardware Reverse Engineering (HRE). This work develops methods for investigating human problem-solving pr…
Do Large Code Models Understand Programming Concepts? Counterfactual Analysis for Code Predicates
Ashish Hooda, Mihai Christodorescu, Miltiadis Allamanis +3
Large Language Models' success on text generation has also made them better at code generation and coding tasks. While a lot of work has demonstrated their remarkable performance o…