491 citations · 652 across the 15 of their papers we have counts for
8 papers · 1 filter
Taking off the Rose-Tinted Glasses: A Critical Look at Adversarial ML Through the Lens of Evasion Attacks
Kevin Eykholt, Farhan Ahmed, Pratik Vaishnavi +1
The vulnerability of machine learning models in adversarial scenarios has garnered significant interest in the academic community over the past decade, resulting in a myriad of att…
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…
Accelerating Certified Robustness Training via Knowledge Transfer
Pratik Vaishnavi, Kevin Eykholt, Amir Rahmati
Training deep neural network classifiers that are certifiably robust against adversarial attacks is critical to ensuring the security and reliability of AI-controlled systems. Alth…
Ares: A System-Oriented Wargame Framework for Adversarial ML
Farhan Ahmed, Pratik Vaishnavi, Kevin Eykholt +1
Since the discovery of adversarial attacks against machine learning models nearly a decade ago, research on adversarial machine learning has rapidly evolved into an eternal war bet…
Transferring Adversarial Robustness Through Robust Representation Matching
Pratik Vaishnavi, Kevin Eykholt, Amir Rahmati
With the widespread use of machine learning, concerns over its security and reliability have become prevalent. As such, many have developed defenses to harden neural networks again…
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…
Robust Classification using Robust Feature Augmentation
Kevin Eykholt, Swati Gupta, Atul Prakash +3
Existing deep neural networks, say for image classification, have been shown to be vulnerable to adversarial images that can cause a DNN misclassification, without any perceptible…
Designing Adversarially Resilient Classifiers using Resilient Feature Engineering
Kevin Eykholt, Atul Prakash
We provide a methodology, resilient feature engineering, for creating adversarially resilient classifiers. According to existing work, adversarial attacks identify weakly correlate…