A measure of robustness against naturally occurring distortions is key to safety, success, and
trustworthiness of machine learning models on deployment. We propose an adversarial …

We propose a Reinforcement Learning (RL) based adversarial Black-box attack (RLAB) that
aims at adding minimum distortion to the input iteratively to deceive image classification …

A measure of robustness against naturally occurring distortions is key to the safety, success,
and trustworthiness of machine learning models on deployment. We investigate an …

Adversarial machine learning (AML) studies the adversarial phenomenon of machine
learning, which may make inconsistent or unexpected predictions with humans. Some …

A measure of robustness against naturally occurring distortions is key to the trustworthiness,
safety, and success of machine learning models on deployment. We investigate an …

We consider the hard-label based black-box adversarial attack setting which solely
observes the target model's predicted class. Most of the attack methods in this setting suffer …

Generating transferable adversarial examples is a challenging issue in adversarial attacks.
Existing works on transferable adversarial examples generation mainly focus on models …

Since the training data for the target model in a data-free black-box attack is not available,
most recent schemes utilize GANs to generate data for training substitute model. However …

Recent works have demonstrated the great performance of forgery image forensics based
on deep learning, but there is still a risk that detectors could be susceptible to unknown …

This paper focuses on an important type of black-box attacks, ie, transfer-based adversarial
attacks, where the adversary generates adversarial examples by a substitute (source) model …