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Hadi Abdullah

2 accepted papers

2025

Deep Learning with Plausible Deniability

NeurIPS 2025poster

Deep learning models are vulnerable to privacy attacks due to their tendency to memorize individual training examples. Theoretically-sound defenses such as differential privacy can defend against this threat, but model performance often suffers. Empirical defenses may thwart existing attacks while m…

Cited by 0Source ↗Scholar ↗
2022

Demystifying Limited Adversarial Transferability in Automatic Speech Recognition Systems

ICLR 2022poster

The targeted transferability of adversarial samples enables attackers to exploit black-box models in the real-world. The most popular method to produce these adversarial samples is optimization attacks, which have been shown to achieve a high level of transferability in some domains. However, recent…

Cited by 19Source ↗PDF ↗Scholar ↗