research
∙
06/14/2023
Efficient Backdoor Attacks for Deep Neural Networks in Real-world Scenarios
Recent deep neural networks (DNNs) have come to rely on vast amounts of ...
share
research
∙
06/14/2023
A Proxy-Free Strategy for Practically Improving the Poisoning Efficiency in Backdoor Attacks
Poisoning efficiency is a crucial factor in poisoning-based backdoor att...
research
∙
04/14/2023
Frequency Decomposition to Tap the Potential of Single Domain for Generalization
Domain generalization (DG), aiming at models able to work on multiple un...
research
∙
04/22/2022
Data-Efficient Backdoor Attacks
Recent studies have proven that deep neural networks are vulnerable to b...
research
∙
11/09/2021
A Statistical Difference Reduction Method for Escaping Backdoor Detection
Recent studies show that Deep Neural Networks (DNNs) are vulnerable to b...
research
∙
11/09/2021
Tightening the Approximation Error of Adversarial Risk with Auto Loss Function Search
Numerous studies have demonstrated that deep neural networks are easily ...
research
∙
03/20/2021
Are High-Frequency Components Beneficial for Training of Generative Adversarial Networks
Advancements in Generative Adversarial Networks (GANs) have the ability ...
research
∙
01/07/2021
Understanding the Error in Evaluating Adversarial Robustness
Deep neural networks are easily misled by adversarial examples. Although...
research
∙
08/29/2020
Improving Resistance to Adversarial Deformations by Regularizing Gradients
Improving the resistance of deep neural networks against adversarial att...
research
∙
02/03/2018
