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20152024
most citedDeepSweep: An Evaluation Framework for Mitigating DNN Backdoor Attacks using Data Augmentation

25 citations · 97 across the 15 of their papers we have counts for

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Showing 2020Show all

7 papers · 1 filter

cs.LG2020★ 15 cited

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,…

cs.CR2020★ 25 cited

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…

cs.CV2020★ 4 cited

Privacy-preserving Collaborative Learning with Automatic Transformation Search

Wei Gao, Shangwei Guo, Tianwei Zhang +3

Collaborative learning has gained great popularity due to its benefit of data privacy protection: participants can jointly train a Deep Learning model without sharing their trainin…

cs.CR2020★ 2 cited

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…

cs.CR2020★ 4 cited

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…

cs.CR2020★ 14 cited

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…

eess.IV2020

Investigating Image Applications Based on Spatial-Frequency Transform and Deep Learning Techniques

Qinkai Zheng, Han Qiu, Gerard Memmi +1

This is the report for the PRIM project in Telecom Paris. This report is about applications based on spatial-frequency transform and deep learning techniques. In this report, there…