We have 3 of 88 papers
We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.
Not the right person? Other researchers publish under this name.
An algorithm is proposed which leverages disentangled semantic factors to generate adversarial perturbation by altering controlled semantic attributes to fool the learner towards various "adversarial" targets.
Large language models (LLMs) are increasingly embedded as core components of data-centric systems, supporting analytical decision making, and automated reasoning over large-scale, heterogeneous datasets. Yet their deployment in open-world environments raises fundamental challenges to security and trustworthiness: LLMs...
The 2nd SeT-LLM Workshop on Secure and Trustworthy Large Language Models brings together researchers and practitioners from data mining, machine learning, security, and responsible AI to address issues from a data-centric, system-level perspective.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
