2 citations · 2 across the 5 of their papers we have counts for
6 papers
Rethinking Brain Decoding with CLIP: The Role of Adversarial Robustness
Byeongseo Bok, Futa Waseda, Jun Liu +1
Brain decoding aims to uncover neural mechanisms by inferring stimulus-related representations from brain signals. In fMRI studies, this is typically achieved by mapping fMRI respo…
Low-Cost Hard-Label Adversarial Attack with Theoretical Foundations
Jun Liu, Leo Yu Zhang, Fengpeng Li +2
Hard-label black-box attacks, relying solely on top-1 predictions, represent one of the most challenging yet practically threat models. Despite recent progress, existing approaches…
LLM Unlearning with LLM Beliefs
Kemou Li, Qizhou Wang, Yue Wang +4
Large language models trained on vast corpora inherently risk memorizing sensitive or harmful content, which may later resurface in their outputs. Prevailing unlearning methods gen…
DifAttack++: Query-Efficient Black-Box Adversarial Attack via Hierarchical Disentangled Feature Space in Cross-Domain
Jun Liu, Jiantao Zhou, Jiandian Zeng +2
This work investigates efficient score-based black-box adversarial attacks that achieve a high Attack Success Rate (ASR) and good generalization ability. We propose a novel attack…
Generating Robust Adversarial Examples against Online Social Networks (OSNs)
Jun Liu, Jiantao Zhou, Haiwei Wu +2
Online Social Networks (OSNs) have blossomed into prevailing transmission channels for images in the modern era. Adversarial examples (AEs) deliberately designed to mislead deep ne…
Recoverable Privacy-Preserving Image Classification through Noise-like Adversarial Examples
Jun Liu, Jiantao Zhou, Jinyu Tian +1
With the increasing prevalence of cloud computing platforms, ensuring data privacy during the cloud-based image related services such as classification has become crucial. In this…