43 citations · 56 across the 11 of their papers we have counts for
13 papers
Dictionary-Aligned Concept Control for Safeguarding Multimodal LLMs
Jinqi Luo, Jinyu Yang, Tal Neiman +5
Multimodal Large Language Models (MLLMs) have been shown to be vulnerable to malicious queries that can elicit unsafe responses. Recent work uses prompt engineering, response class…
SECA: Semantically Equivalent and Coherent Attacks for Eliciting LLM Hallucinations
Buyun Liang, Liangzu Peng, Jinqi Luo +3
Large Language Models (LLMs) are increasingly deployed in high-risk domains. However, state-of-the-art LLMs often exhibit hallucinations, raising serious concerns about their relia…
Voyaging into Perpetual Dynamic Scenes from a Single View
Fengrui Tian, Tianjiao Ding, Jinqi Luo +2
The problem of generating a perpetual dynamic scene from a single view is an important problem with widespread applications in augmented and virtual reality, and robotics. However,…
LogiCity: Advancing Neuro-Symbolic AI with Abstract Urban Simulation
Bowen Li, Zhaoyu Li, Qiwei Du +10
Recent years have witnessed the rapid development of Neuro-Symbolic (NeSy) AI systems, which integrate symbolic reasoning into deep neural networks. However, most of the existing b…
Unsupervised Model Diagnosis
Yinong Oliver Wang, Eileen Li, Jinqi Luo +2
Ensuring model explainability and robustness is essential for reliable deployment of deep vision systems. Current methods for evaluating robustness rely on collecting and annotatin…
PaCE: Parsimonious Concept Engineering for Large Language Models
Jinqi Luo, Tianjiao Ding, Kwan Ho Ryan Chan +4
Large Language Models (LLMs) are being used for a wide variety of tasks. While they are capable of generating human-like responses, they can also produce undesirable output includi…
Contextual Knowledge Pursuit for Faithful Visual Synthesis
Jinqi Luo, Kwan Ho Ryan Chan, Dimitris Dimos +1
Modern text-to-vision generative models often hallucinate when the prompt describing the scene to be generated is underspecified. In large language models (LLMs), a prevalent strat…
Zero-shot Model Diagnosis
Jinqi Luo, Zhaoning Wang, Chen Henry Wu +2
When it comes to deploying deep vision models, the behavior of these systems must be explicable to ensure confidence in their reliability and fairness. A common approach to evaluat…
Semantic Image Attack for Visual Model Diagnosis
Jinqi Luo, Zhaoning Wang, Chen Henry Wu +2
In practice, metric analysis on a specific train and test dataset does not guarantee reliable or fair ML models. This is partially due to the fact that obtaining a balanced, divers…
Inconspicuous Adversarial Patches for Fooling Image Recognition Systems on Mobile Devices
Tao Bai, Jinqi Luo, Jun Zhao
Deep learning based image recognition systems have been widely deployed on mobile devices in today's world. In recent studies, however, deep learning models are shown vulnerable to…
Recent Advances in Adversarial Training for Adversarial Robustness
Tao Bai, Jinqi Luo, Jun Zhao +2
Adversarial training is one of the most effective approaches defending against adversarial examples for deep learning models. Unlike other defense strategies, adversarial training…
Recent Advances in Understanding Adversarial Robustness of Deep Neural Networks
Tao Bai, Jinqi Luo, Jun Zhao
Adversarial examples are inevitable on the road of pervasive applications of deep neural networks (DNN). Imperceptible perturbations applied on natural samples can lead DNN-based c…
Generating Adversarial yet Inconspicuous Patches with a Single Image
Jinqi Luo, Tao Bai, Jun Zhao
Deep neural networks have been shown vulnerable toadversarial patches, where exotic patterns can resultin models wrong prediction. Nevertheless, existing ap-proaches to adversarial…