228 citations · 329 across the 13 of their papers we have counts for
13 papers
Toward Cybersecurity-Expert Small Language Models
Matan Levi, Daniel Ohayon, Ariel Blobstein +3
Large language models (LLMs) are transforming everyday applications, yet deployment in cybersecurity lags due to a lack of high-quality, domain-specific models and training dataset…
URET: Universal Robustness Evaluation Toolkit (for Evasion)
Kevin Eykholt, Taesung Lee, Douglas Schales +3
Machine learning models are known to be vulnerable to adversarial evasion attacks as illustrated by image classification models. Thoroughly understanding such attacks is critical i…
Adaptive Verifiable Training Using Pairwise Class Similarity
Shiqi Wang, Kevin Eykholt, Taesung Lee +2
Verifiable training has shown success in creating neural networks that are provably robust to a given amount of noise. However, despite only enforcing a single robustness criterion…
Adversarial Examples and Metrics
Nico Döttling, Kathrin Grosse, Michael Backes +1
Adversarial examples are a type of attack on machine learning (ML) systems which cause misclassification of inputs. Achieving robustness against adversarial examples is crucial to…
Backdoor Smoothing: Demystifying Backdoor Attacks on Deep Neural Networks
Kathrin Grosse, Taesung Lee, Battista Biggio +3
Backdoor attacks mislead machine-learning models to output an attacker-specified class when presented a specific trigger at test time. These attacks require poisoning the training…
Reaching Data Confidentiality and Model Accountability on the CalTrain
Zhongshu Gu, Hani Jamjoom, Dong Su +5
Distributed collaborative learning (DCL) paradigms enable building joint machine learning models from distrusting multi-party participants. Data confidentiality is guaranteed by re…