2 citations · 4 across the 7 of their papers we have counts for
8 papers
Universal Cross-Prompt Adversarial Attacks on Promptable Concept Segmentation
Ziqi Zhou, Yifan Hu, Yufei Song +5
The Segment Anything Model (SAM) achieves remarkable performance in visual segmentation. The latest SAM3 extends promptable segmentation to concept-level prediction, broadening the…
Vanish into Thin Air: Cross-prompt Universal Adversarial Attacks for SAM2
Ziqi Zhou, Yifan Hu, Yufei Song +6
Recent studies reveal the vulnerability of the image segmentation foundation model SAM to adversarial examples. Its successor, SAM2, has attracted significant attention due to its…
DarkHash: A Data-Free Backdoor Attack Against Deep Hashing
Ziqi Zhou, Menghao Deng, Yufei Song +6
Benefiting from its superior feature learning capabilities and efficiency, deep hashing has achieved remarkable success in large-scale image retrieval. Recent studies have demonstr…
NumbOD: A Spatial-Frequency Fusion Attack Against Object Detectors
Ziqi Zhou, Bowen Li, Yufei Song +6
With the advancement of deep learning, object detectors (ODs) with various architectures have achieved significant success in complex scenarios like autonomous driving. Previous ad…
DarkSAM: Fooling Segment Anything Model to Segment Nothing
Ziqi Zhou, Yufei Song, Minghui Li +5
Segment Anything Model (SAM) has recently gained much attention for its outstanding generalization to unseen data and tasks. Despite its promising prospect, the vulnerabilities of…
Variational Bayes for Federated Continual Learning
Dezhong Yao, Sanmu Li, Yutong Dai +4
Federated continual learning (FCL) has received increasing attention due to its potential in handling real-world streaming data, characterized by evolving data distributions and va…
Securely Fine-tuning Pre-trained Encoders Against Adversarial Examples
Ziqi Zhou, Minghui Li, Wei Liu +7
With the evolution of self-supervised learning, the pre-training paradigm has emerged as a predominant solution within the deep learning landscape. Model providers furnish pre-trai…
Revisiting Gradient Pruning: A Dual Realization for Defending against Gradient Attacks
Lulu Xue, Shengshan Hu, Ruizhi Zhao +4
Collaborative learning (CL) is a distributed learning framework that aims to protect user privacy by allowing users to jointly train a model by sharing their gradient updates only.…