39 citations · 171 across the 11 of their papers we have counts for
5 papers · 1 filter
FenceBox: A Platform for Defeating Adversarial Examples with Data Augmentation Techniques
Han Qiu, Yi Zeng, Tianwei Zhang +2
It is extensively studied that Deep Neural Networks (DNNs) are vulnerable to Adversarial Examples (AEs). With more and more advanced adversarial attack methods have been developed,…
DeepSweep: An Evaluation Framework for Mitigating DNN Backdoor Attacks using Data Augmentation
Han Qiu, Yi Zeng, Shangwei Guo +3
Public resources and services (e.g., datasets, training platforms, pre-trained models) have been widely adopted to ease the development of Deep Learning-based applications. However…
Fine-tuning Is Not Enough: A Simple yet Effective Watermark Removal Attack for DNN Models
Shangwei Guo, Tianwei Zhang, Han Qiu +3
Watermarking has become the tendency in protecting the intellectual property of DNN models. Recent works, from the adversary's perspective, attempted to subvert watermarking mechan…
A Data Augmentation-based Defense Method Against Adversarial Attacks in Neural Networks
Yi Zeng, Han Qiu, Gerard Memmi +1
Deep Neural Networks (DNNs) in Computer Vision (CV) are well-known to be vulnerable to Adversarial Examples (AEs), namely imperceptible perturbations added maliciously to cause wro…
Mitigating Advanced Adversarial Attacks with More Advanced Gradient Obfuscation Techniques
Han Qiu, Yi Zeng, Qinkai Zheng +3
Deep Neural Networks (DNNs) are well-known to be vulnerable to Adversarial Examples (AEs). A large amount of efforts have been spent to launch and heat the arms race between the at…