Adversarial examples have attracted significant attention in machine learning, but the
reasons for their existence and pervasiveness remain unclear. We demonstrate that …

The adversarial machine learning literature is largely partitioned into evasion attacks on
testing data and poisoning attacks on training data. In this work, we show that adversarial …

A wide variety of works have explored the reason for the existence of adversarial examples,
but there is no consensus on the explanation. We propose to treat the DNN logits as a vector …

Availability attacks, which poison the training data with imperceptible perturbations, can
make the data not exploitable by machine learning algorithms so as to prevent unauthorized …

Delusive attacks aim to substantially deteriorate the test accuracy of the learning model by
slightly perturbing the features of correctly labeled training examples. By formalizing 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 …

Deep neural networks are vulnerable to adversarial examples (AEs), which have adversarial
transferability: AEs generated for the source model can mislead another (target) model's …

Machine learning fairness concerns about the biases towards certain protected or sensitive
group of people when addressing the target tasks. This paper studies the debiasing problem …

We investigate how the population nonlinearities resulting from lateral inhibition and
thresholding in sparse coding networks influence neural response selectivity and …

Recent works have demonstrated that current deep neural networks suffer from small but
intentional perturbation during the testing phase of the model. Such perturbations aiming at …