Achieving robustness to distributional shift is a longstanding and challenging goal of
computer vision. Data augmentation is a commonly used approach for improving …

Image compression-based approaches for defending against the adversarial-example
attacks, which threaten the safety use of deep neural networks (DNN), have been …

Over the last few years, the phenomenon of adversarial examples—maliciously constructed
inputs that fool trained machine learning models—has captured the attention of the research …

Over the last few years, the phenomenon of adversarial examples---maliciously constructed
inputs that fool trained machine learning models---has captured the attention of the research …

Harm reporting in Artificial Intelligence (AI) currently lacks a structured process for disclosing
and addressing algorithmic flaws, relying largely on an ad-hoc approach. This contrasts …

Abstract Harm reporting in Artificial Intelligence (AI) currently lacks a structured process for
disclosing and addressing algorithmic flaws, relying largely on an ad-hoc approach. This …

The booming interest in adversarial attacks stems from a misalignment between human
vision and a deep neural network (DNN),\ie~ a human imperceptible perturbation fools the …

Recent research works showed that deep neural networks are vulnerable to adversarial
examples, which are usually maliciously created by carefully adding deliberate and …

Individually reinforcing the robustness of a single deep learning model only gives limited
security guarantees especially when facing adversarial examples. In this article, we propose …

In recent years, cyber-attacks against individuals, businesses, and organizations have
increased. Cyber criminals are always looking for effective vectors to deliver malware to …