Deep learning models are often trained on distributed, web-scale datasets crawled from the
internet. In this paper, we introduce two new dataset poisoning attacks that intentionally …

The swift proliferation of Internet of Things (IoT) devices has presented considerable
challenges in maintaining cybersecurity. As IoT ecosystems expand, they increasingly attract …

Bioprinting is a versatile technology which is gaining rapid adoption in healthcare fields
such as tissue engineering, regenerative medicine, drug delivery, and surgical planning …

Backdoor attacks are an important type of adversarial threat against deep neural network
classifiers, wherein test samples from one or more source classes will be (mis) classified to …

Recent years have seen a proliferation of research on adversarial machine learning.
Numerous papers demonstrate powerful algorithmic attacks against a wide variety of …

Machine learning models may inadvertently memorize sensitive, unauthorized, or malicious
data, posing risks of privacy breaches, security vulnerabilities, and performance …

Reproducibility is crucial to the advancement of science; it strengthens confidence in
seemingly contradictory results and expands the boundaries of known discoveries …

Recent works have demonstrated that deep learning models are vulnerable to backdoor
poisoning attacks, where these attacks instill spurious correlations to external trigger …

Abstract This NIST Trustworthy and Responsible AI report develops a taxonomy of concepts
and defines terminology in the field of adversarial machine learning (AML). The taxonomy is …

AI-based code generators have become pivotal in assisting developers in writing software
starting from natural language (NL). However, they are trained on large amounts of data …