2 citations · 7 across the 16 of their papers we have counts for
21 papers
From Expected Harmfulness to Likelihood: A Probabilistic Reformulation of Jailbreaking LLM Agents
Juanyang Xu, Zheng Wang, Xingyu Zhao +2
When the harmfulness of an LLM agent's output can be quantified, a natural jailbreaking objective is to maximize expected harmfulness over admissible input modifications. An altern…
Probabilistic Adversarial Training
Andi Zhang, Xingyu Zhao, Siddartha Khastgir
Building on a probabilistic perspective in which adversarial examples arise from the overlap between a distance-based distribution and a victim-classifier-induce…
Let the Carrier Carry the Attack: Preserving the Subject in Adversarial Image Generation
Linfeng Jiang, Steven McDonagh, Yuhang Chen +3
Strong unrestricted adversarial attacks can distort the primary object of an image, hereafter referred to as the subject. To preserve subject integrity without compromising attack…
Reusing Operational Evidence After Context Changes: A Conservative Bayesian Framework for Autonomous Vehicle Safety
Robab Aghazadeh Chakherlou, Siddartha Khastgir, Xingyu Zhao
Operational evidence, i.e., evidence of operation without failure is an important component of confidence in the safety or reliability of a system in service, but it is costly to c…
Modeling Memory-Dependent Reliability of LLMs: A Hidden Markov Model
Robab Aghazadeh Chakherlou, Siddartha Khastgir, Peter Popov +1
Reliability assessment of large language models (LLMs) seeks to estimate the probability that a model produces correct responses under a specified operational profile. Conventional…
Probabilistic Robustness in Medical Image Classification
Yi Zhang, Siddartha Khastgir, Xingyu Zhao
Deep learning (DL) has shown strong performance in medical image classification, but its trustworthy deployment remains challenging in safety-critical clinical settings, where pred…
Quantifying Fidelity: A Decisive Feature Approach to Comparing Synthetic and Real Imagery
Danial Safaei, Siddartha Khastgir, Mohsen Alirezaei +4
Virtual testing using synthetic data has become a cornerstone of autonomous vehicle (AV) safety assurance. Despite progress in improving visual realism through advanced simulators…
Introducing a Novel Systems Thinking approach inspired by STPA: Road Safety Intervention design case study
Halima El Badaoui, Siddartha Khastgir, Mariat James Elizebeth +3
According to the latest provisional statistics released by the UK Department for Transport, Great Britain recorded 1,633 road deaths in 2024, representing a slight increase from 20…
Non-Parametric Probabilistic Robustness: A Conservative Risk Estimator under Unknown Perturbation Distributions
Zheng Wang, Yi Zhang, Siddartha Khastgir +2
Deep learning (DL) models, despite their remarkable success, remain vulnerable to small input perturbations that can cause erroneous outputs, motivating the recent proposal of prob…
Uncertainty-Aware Measurement of Scenario Suite Representativeness for Autonomous Systems
Robab Aghazadeh Chakherlou, Siddartha Khastgir, Xingyu Zhao +2
Assuring the trustworthiness and safety of AI systems, e.g., autonomous vehicles (AV), depends critically on the data-related safety properties, e.g., representativeness, completen…
PRBench: A Standardized Probabilistic Robustness Benchmark
Yi Zhang, Zheng Wang, Zhen Chen +5
Deep learning models are notoriously vulnerable to imperceptible perturbations. Most existing research centers on adversarial robustness (AR), which evaluates models under worst-ca…
A Hierarchical Imprecise Probability Approach to Reliability Assessment of Large Language Models
Robab Aghazadeh-Chakherlou, Qing Guo, Siddartha Khastgir +3
Large Language Models (LLMs) are increasingly deployed across diverse domains, raising the need for rigorous reliability assessment methods. Existing benchmark-based evaluations pr…
Safety Analysis of eVTOL Operations based on STPA
Mariat James Elizebeth, Shufeng Chen, Halima El Badaoui +2
Electric Vertical Take-Off and Landing (eVTOL) aircraft are expected to be quieter and more cost-effective than helicopters, offering major economic and social benefits through imp…
Toward Safe and Energy-Efficient 5G NR V2X Communications in Rural Environments
Zhanle Zhao, Son Dinh-Van, Yuen Kwan Mo +2
Connected braking can reduce fatal collisions in connected and autonomous vehicles (CAVs) by using reliable, low-latency 5G New Radio (NR) links, especially NR Sidelink Vehicle-to-…
A Scalable Framework for Safety Assurance of Self-Driving Vehicles based on Assurance 2.0
Shufeng Chen, Mariat James Elizebeth, Robab Aghazadeh Chakherlou +4
Assurance 2.0 is a modern framework developed to address the assurance challenges of increasingly complex, adaptive, and autonomous systems. Building on the traditional Claims-Argu…
A Scalable Framework for the Management of STPA Requirements: a Case Study on eVTOL Operations
Shufeng Chen, Halima El Badaoui, Mariat James Elizebeth +3
System-Theoretic Process Analysis (STPA) is a recommended method for analysing complex systems, capable of identifying thousands of safety requirements often missed by traditional…
Self-driving technologies need the help of the public: A narrative review of the evidence
Jonathan Smith, Siddartha Khastgir
If public trust is lost in a new technology early in its life cycle it can take much more time for the benefits of that technology to be realised. Eventually tens-of-millions of pe…
Trustworthy Text-to-Image Diffusion Models: A Timely and Focused Survey
Yi Zhang, Zhen Chen, Chih-Hong Cheng +6
Text-to-Image (T2I) Diffusion Models (DMs) have garnered widespread attention for their impressive advancements in image generation. However, their growing popularity has raised et…
ProTIP: Probabilistic Robustness Verification on Text-to-Image Diffusion Models against Stochastic Perturbation
Yi Zhang, Yun Tang, Wenjie Ruan +4
Text-to-Image (T2I) Diffusion Models (DMs) have shown impressive abilities in generating high-quality images based on simple text descriptions. However, as is common with many Deep…
Domain Knowledge Distillation from Large Language Model: An Empirical Study in the Autonomous Driving Domain
Yun Tang, Antonio A. Bruto da Costa, Jason Zhang +3
Engineering knowledge-based (or expert) systems require extensive manual effort and domain knowledge. As Large Language Models (LLMs) are trained using an enormous amount of cross-…
STPA for Learning-Enabled Systems: A Survey and A New Practice
Yi Qi, Yi Dong, Siddartha Khastgir +3
Systems Theoretic Process Analysis (STPA) is a systematic approach for hazard analysis that has been used across many industrial sectors including transportation, energy, and defen…