Search Results for author:
Found 8 papers, 6 papers with code
Investigating White-Box Attacks for On-Device Models
Our findings emphasize the need for developers to carefully consider their model deployment strategies, and use white-box methods to evaluate the vulnerability of on-device models.
Concealing Sensitive Samples against Gradient Leakage in Federated Learning
Federated Learning (FL) is a distributed learning paradigm that enhances users privacy by eliminating the need for clients to share raw, private data with the server.
Performance Evaluation of Adversarial Attacks: Discrepancies and Solutions
Recently, adversarial attack methods have been developed to challenge the robustness of machine learning models.
Local Label Point Correction for Edge Detection of Overlapping Cervical Cells
The qualitative and quantitative experimental results show that our LLPC can improve the quality of manual labels and the accuracy of overlapping cell edge detection.
Decision-based Universal Adversarial Attack
A single perturbation can pose the most natural images to be misclassified by classifiers.
ProbaNet: Proposal-balanced Network for Object Detection
Candidate object proposals generated by object detectors based on convolutional neural network (CNN) encounter easy-hard samples imbalance problem, which can affect overall performance.
Adversarial Imitation Attack
Then, the adversarial examples generated by the imitation model are utilized to fool the attacked model.
DaST: Data-free Substitute Training for Adversarial Attacks
In this paper, we propose a data-free substitute training method (DaST) to obtain substitute models for adversarial black-box attacks without the requirement of any real data.
