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Shuyu Cheng

7 accepted papers

2026

Improving the Convergence Rate of Ray Search Optimization for Query-Efficient Hard-Label Attacks

AAAI 2026technical

In hard-label black-box adversarial attacks, where only the top-1 predicted label is accessible, the prohibitive query complexity poses a major obstacle to practical deployment. In this paper, we focus on optimizing a representative class of attacks that search for the optimal ray direction yielding

Cited by 0SourcePDFScholar
2025

Boosting Ray Search Procedure of Hard-label Attacks with Transfer-based Priors

ICLR 2025spotlight

One of the most practical and challenging types of black-box adversarial attacks is the hard-label attack, where only the top-1 predicted label is available. One effective approach is to search for the optimal ray direction from the benign image that minimizes the $\ell_p$ norm distance to the adver…

Cited by 0SourcePDFScholar
2024

Efficient Black-box Adversarial Attacks via Bayesian Optimization Guided by a Function Prior

ICML 2024poster

This paper studies the challenging black-box adversarial attack that aims to generate adversarial examples against a black-box model by only using output feedback of the model to input queries. Some previous methods improve the query efficiency by incorporating the gradient of a surrogate white-box…

2021

On the Convergence of Prior-Guided Zeroth-Order Optimization Algorithms

NeurIPS 2021poster

Zeroth-order (ZO) optimization is widely used to handle challenging tasks, such as query-based black-box adversarial attacks and reinforcement learning. Various attempts have been made to integrate prior information into the gradient estimation procedure based on finite differences, with promising e…

2020

A Wasserstein Minimum Velocity Approach to Learning Unnormalized Models

AISTATS 2020poster

Score matching provides an effective approach to learning flexible unnormalized models, but its scalability is limited by the need to evaluate a second-order derivative. In this paper, we present a scalable approximation to a general family of learning objectives including score matching, by observi…

2020

Defense Against Adversarial Attacks via Controlling Gradient Leaking on Embedded Manifolds

ECCV 2020poster

Deep neural networks are vulnerable to adversarial attacks. Though various attempts have been made, it is still largely open to fully understand the existence of adversarial samples and thereby develop effective defense strategies. In this paper, we present a new perspective, namely gradient leaking…

Cited by 26SourcePDFScholar
2019

Improving Black-box Adversarial Attacks with a Transfer-based Prior

NeurIPS 2019poster

We consider the black-box adversarial setting, where the adversary has to generate adversarial perturbations without access to the target models to compute gradients. Previous methods tried to approximate the gradient either by using a transfer gradient of a surrogate white-box model, or based on th…