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20162026
most citedCharacterizing Adversarial Subspaces Using Local Intrinsic Dimensionality

485 citations · 1.5k across the 72 of their papers we have counts for

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Showing 2024Show all

7 papers · 1 filter

cs.CV2024★ 3 cited

Visual-Text Cross Alignment: Refining the Similarity Score in Vision-Language Models

Jinhao Li, Haopeng Li, Sarah Erfani +3

It has recently been discovered that using a pre-trained vision-language model (VLM), e.g., CLIP, to align a whole query image with several finer text descriptions generated by a l…

cs.LG2024★ 1 cited

OIL-AD: An Anomaly Detection Framework for Sequential Decision Sequences

Chen Wang, Sarah Erfani, Tansu Alpcan +1

Anomaly detection in decision-making sequences is a challenging problem due to the complexity of normality representation learning and the sequential nature of the task. Most exist…

cs.LG2024

Unlearnable Examples For Time Series

Yujing Jiang, Xingjun Ma, Sarah Monazam Erfani +1

Unlearnable examples (UEs) refer to training samples modified to be unlearnable to Deep Neural Networks (DNNs). These examples are usually generated by adding error-minimizing nois…

cs.CL2024★ 1 cited

Round Trip Translation Defence against Large Language Model Jailbreaking Attacks

Canaan Yung, Hadi Mohaghegh Dolatabadi, Sarah Erfani +1

Large language models (LLMs) are susceptible to social-engineered attacks that are human-interpretable but require a high level of comprehension for LLMs to counteract. Existing de…

cs.LG2024

Be Persistent: Towards a Unified Solution for Mitigating Shortcuts in Deep Learning

Hadi M. Dolatabadi, Sarah M. Erfani, Christopher Leckie

Deep neural networks (DNNs) are vulnerable to shortcut learning: rather than learning the intended task, they tend to draw inconclusive relationships between their inputs and outpu…

cs.LG2024

End-to-End Anti-Backdoor Learning on Images and Time Series

Yujing Jiang, Xingjun Ma, Sarah Monazam Erfani +2

Backdoor attacks present a substantial security concern for deep learning models, especially those utilized in applications critical to safety and security. These attacks manipulat…