Backdoor attack poses a significant security threat to Deep Learning applications. Existing
attacks are often not evasive to established backdoor detection techniques. This …

Recent literature has shown that LLMs are vulnerable to backdoor attacks, where malicious
attackers inject a secret token sequence (ie, trigger) into training prompts and enforce their …

Deep neural networks (DNNs) have demonstrated effectiveness in various fields. However,
DNNs are vulnerable to backdoor attacks, which inject a unique pattern, called trigger, into …

Object detection models, widely used in security-critical applications, are vulnerable to
backdoor attacks that cause targeted misclassifications when triggered by specific patterns …

An image encoder pre-trained by self-supervised learning can be used as a general-
purpose feature extractor to build downstream classifiers for various downstream tasks …

Learning utility, or reward, models from pairwise comparisons is a fundamental component
in a number of application domains. These approaches inherently entail collecting …

The exploration of backdoor vulnerabilities in object detectors, particularly in real-world
scenarios, remains limited. A significant challenge lies in the absence of a natural physical …