We study the problem of adversarially robust learning in the transductive setting. For classes
H of bounded VC dimension, we propose a simple transductive learner that when presented …

Malware authors focus on deceiving and evading Anti Malware Engines (AME). Evasion
attacks take in malware samples and modify those samples to by-pass ml based AME …

We formalize and extend existing definitions of backdoor-based watermarks and adversarial
defenses as interactive protocols between two players. The existence of these schemes is …

The Transductive Support Vector Machine (TSVM) is an effective semi-supervised learning
algorithm vulnerable to adversarial sample attacks. This paper proposes a new adversarial …

In the context of adversarial robustness, we make three strongly related contributions. First,
we prove that while attacking ReLU classifiers is $\mathit {NP} $-hard, ensuring their …

Adversarial machine learning has been extensively studied from perspectives of attack
settings and defense strategies. However, existing adversarial training models fail to be …

Both transduction and rejection have emerged as key techniques to enable stronger
defenses against adversarial perturbations, but existing work has not investigated the …