A* Attack: A Novel Path-Finding Approach to Adversarial Examples
Abstract
This paper presents a novel approach to exploiting a key vulnerability of deep neural networks (DNNs)to adversarial examples with a focus on the black-box machine learning as a service (MLaaS) environment.We introduce A* Attack, a unique adversarial example attack that leverages the A* Search algorithm to find adversarial perturbations. This innovative approach is designed to overcome the challenges of both excessive model queries in decision- and score-based attacks and the limitations of transferability from white-box attacks. The A* Attack demonstrates competitive performance in the white-box setting and sets a new standard in the decision-based black-box setting, achieving high attack success rates with minimal queries. This represents a significant advancement in the field, offering a new approach to the black-box attack method. This paper provides a competitive evaluation of the A* Attack on CIFAR10 and ImageNet, comparing its performance against other leading attacks and defenses.
Document Details
- Document Type
- Technical Report
- Publication Date
- Sep 01, 2023
- Accession Number
- AD1224614
Entities
People
- Christopher D Clark
Organizations
- Naval Postgraduate School