7 citations · 11 across the 19 of their papers we have counts for
11 papers · 1 filter
Dual-View Inference Attack: Machine Unlearning Amplifies Privacy Exposure
Lulu Xue, Shengshan Hu, Linqiang Qian +6
Machine unlearning is a newly popularized technique for removing specific training data from a trained model, enabling it to comply with data deletion requests. While it protects t…
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…
SegTrans: Transferable Adversarial Examples for Segmentation Models
Yufei Song, Ziqi Zhou, Qi Lu +6
Segmentation models exhibit significant vulnerability to adversarial examples in white-box settings, but existing adversarial attack methods often show poor transferability across…
Towards Real-World Deepfake Detection: A Diverse In-the-wild Dataset of Forgery Faces
Junyu Shi, Minghui Li, Junguo Zuo +8
Deepfakes, leveraging advanced AIGC (Artificial Intelligence-Generated Content) techniques, create hyper-realistic synthetic images and videos of human faces, posing a significant…
MARS: A Malignity-Aware Backdoor Defense in Federated Learning
Wei Wan, Yuxuan Ning, Zhicong Huang +7
Federated Learning (FL) is a distributed paradigm aimed at protecting participant data privacy by exchanging model parameters to achieve high-quality model training. However, this…
ADVEDM:Fine-grained Adversarial Attack against VLM-based Embodied Agents
Yichen Wang, Hangtao Zhang, Hewen Pan +7
Vision-Language Models (VLMs), with their strong reasoning and planning capabilities, are widely used in embodied decision-making (EDM) tasks in embodied agents, such as autonomous…
Towards Reliable Forgetting: A Survey on Machine Unlearning Verification
Lulu Xue, Shengshan Hu, Wei Lu +7
With growing demands for privacy protection, security, and legal compliance (e.g., GDPR), machine unlearning has emerged as a critical technique for ensuring the controllability an…
Uncertainty-Aware Metabolic Stability Prediction with Dual-View Contrastive Learning
Peijin Guo, Minghui Li, Hewen Pan +6
Accurate prediction of molecular metabolic stability (MS) is critical for drug research and development but remains challenging due to the complex interplay of molecular interactio…
Secure Transfer Learning: Training Clean Models Against Backdoor in (Both) Pre-trained Encoders and Downstream Datasets
Yechao Zhang, Yuxuan Zhou, Tianyu Li +4
Transfer learning from pre-trained encoders has become essential in modern machine learning, enabling efficient model adaptation across diverse tasks. However, this combination of…
Improving Generalization of Universal Adversarial Perturbation via Dynamic Maximin Optimization
Yechao Zhang, Yingzhe Xu, Junyu Shi +4
Deep neural networks (DNNs) are susceptible to universal adversarial perturbations (UAPs). These perturbations are meticulously designed to fool the target model universally across…
PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation
Yufei Song, Ziqi Zhou, Minghui Li +6
With the rapid advancement of deep learning, the model robustness has become a significant research hotspot, \ie, adversarial attacks on deep neural networks. Existing works primar…