Proceedings of the International Conference on Learning Representations (ICLR), 2026
We propose Jailbreaking Using LLM Introspection (JULI), which jailbreaks LLMs by manipulating the token log probabilities, using a tiny plug-in block, BiasNet.
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Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 9, Issue 1, 2025
This paper presents a novel source-free domain adaptation framework for HAR that effectively handles substantial domain discrepancies across different datasets.
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Proceedings of IEEE Transactions on Dependable and Secure Computing, Volume: 22, Issue: 2, March-April 2025, 2024
In this work, we propose a novel backdoor defense approach called ARTEMIS, which utilizes distribution shifts to eliminate the discrepancy between poisoned and benign samples in the feature space.
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