Deep learning technology is increasingly being applied in safety-critical scenarios but has
recently been found to be susceptible to imperceptible adversarial perturbations. This raises …

Explaining the decisions of an Artificial Intelligence (AI) model is increasingly critical in many
real-world, high-stake applications. Hundreds of papers have either proposed new feature …

Deep learning plays a significant role in develo** a robust and constructive framework for
tackling complex learning tasks. Consequently, it is widely utilized in many security-critical …

Recent studies in deep neural networks have shown that injecting random noise in the input
layer of the networks contributes towards ℓ p-norm-bounded adversarial perturbations …

Perturbation-based explanation methods often measure the contribution of an input feature
to an image classifier's outputs by heuristically removing it via eg blurring, adding noise, or …

Adversarial data examples have drawn significant attention from the machine learning and
security communities. A line of work on tackling adversarial examples is certified robustness …

To evaluate the robustness gain of Bayesian neural networks on image classification tasks,
we perform input perturbations, and adversarial attacks to the state-of-the-art Bayesian …

The integration of an ensemble of deep learning models has been extensively explored to
enhance defense against adversarial attacks. The diversity among sub-models increases …

As machine learning models are increasingly being employed in various high-stakes
settings, it becomes important to ensure that predictions of these models are not only …

Image-based object detection and classification are essential for satellite-based monitoring,
which spans multiple safety-critical engineering applications. Meanwhile, state-of-the-art …