Deep learning has been widely applied in various fields such as computer vision, natural
language processing, and data mining. Although deep learning has achieved significant …

Abstract We propose the Square Attack, a score-based black-box l_2 l 2-and l_ ∞ l∞-
adversarial attack that does not rely on local gradient information and thus is not affected by …

Deep neural networks have achieved great success in many important remote sensing
tasks. Nevertheless, their vulnerability to adversarial examples should not be neglected. In …

Deep neural networks (DNNs) have demonstrated excellent performance on various tasks,
yet they are under the risk of adversarial examples that can be easily generated when the …

Adversarial attacks on deep neural networks (DNNs) have been found for several years.
However, the existing adversarial attacks have high success rates only when the information …

We propose a versatile framework based on random search, Sparse-RS, for score-based
sparse targeted and untargeted attacks in the black-box setting. Sparse-RS does not rely on …

Data Poisoning Attacks (DPA) represent a sophisticated technique aimed at distorting the
training data of machine learning models, thereby manipulating their behavior. This process …

End-to-end encryption (E2EE) by messaging platforms enable people to securely and
privately communicate with one another. Its widespread adoption however raised concerns …

Adversarial attacks based on randomized search schemes have obtained state-of-the-art
results in black-box robustness evaluation recently. However, as we demonstrate in this …

Neural classifiers have achieved near human level performances when applied to several
real-world tasks. Despite their successes, recent works have demonstrated their vulnerability …