Write a Blog >>
ISSTA 2021
Sun 11 - Sat 17 July 2021 Online
co-located with ECOOP and ISSTA 2021

Deep Learning (DL) system has been widely used in many critical applications, such as autonomous vehicles and unmanned aerial vehicles.
However, their security is threatened by backdoor attack, which is achieved by adding artificial patterns on specific training data.
Existing attack methods normally poison the data using a patch, and they can be easily detected by existing detection methods.
In this work, we propose the Adversarial Backdoor, which utilizes the Targeted Universal Adversarial Perturbation (TUAP) to hide the anomalies in DL models and confuse existing powerful detection methods.
With extensive experiments, it is demonstrated that Adversarial Backdoor can be injected stably with an attack success rate around 98%.
Moreover, Adversarial Backdoor can bypass state-of-the-art backdoor detection methods. More specifically, only around 37% of the poisoned models can be caught, and less than 29% of the poisoned data cannot bypass the detection.
In contrast, for the patch backdoor, all the poisoned models and more than 80% of the poisoned data will be detected.
This work intends to alarm the researchers and developers of this potential threat and to inspire the designing of effective detection methods.

Fri 16 Jul

Displayed time zone: Brussels, Copenhagen, Madrid, Paris change

02:00 - 03:20
Session 13 (time band 2) Testing Deep Learning Systems 4Technical Papers at ISSTA 1
Chair(s): Shiqing Ma Rutgers University
02:00
20m
Talk
Efficient White-Box Fairness Testing through Gradient Search
Technical Papers
Lingfeng Zhang East China Normal University, Yueling Zhang Singapore Management University, Min Zhang East China Normal University
DOI Media Attached
02:20
20m
Talk
DialTest: Automated Testing for Recurrent-Neural-Network-Driven Dialogue Systems
Technical Papers
Zixi Liu Nanjing University, Yang Feng Nanjing University, Zhenyu Chen Nanjing University
DOI
02:40
20m
Talk
AdvDoor: Adversarial Backdoor Attack of Deep Learning System
Technical Papers
Quan Zhang Tsinghua University, Yifeng Ding Tsinghua University, Yongqiang Tian Tianjin University, Jianmin Guo Tsinghua University, Min Yuan WeBank, Yu Jiang Tsinghua University
DOI
03:00
20m
Talk
ModelDiff: Testing-Based DNN Similarity Comparison for Model Reuse Detection
Technical Papers
Yuanchun Li Microsoft Research, Ziqi Zhang Peking University, Bingyan Liu Peking University, Ziyue Yang Microsoft Research, Yunxin Liu Tsinghua University
DOI

Sat 17 Jul

Displayed time zone: Brussels, Copenhagen, Madrid, Paris change

08:00 - 09:20
Session 26 (time band 3) Testing Deep Learning Systems 5Technical Papers at ISSTA 2
Chair(s): Junjie Chen Tianjin University
08:00
20m
Talk
Efficient White-Box Fairness Testing through Gradient Search
Technical Papers
Lingfeng Zhang East China Normal University, Yueling Zhang Singapore Management University, Min Zhang East China Normal University
DOI Media Attached
08:20
20m
Talk
DialTest: Automated Testing for Recurrent-Neural-Network-Driven Dialogue Systems
Technical Papers
Zixi Liu Nanjing University, Yang Feng Nanjing University, Zhenyu Chen Nanjing University
DOI
08:40
20m
Talk
AdvDoor: Adversarial Backdoor Attack of Deep Learning System
Technical Papers
Quan Zhang Tsinghua University, Yifeng Ding Tsinghua University, Yongqiang Tian Tianjin University, Jianmin Guo Tsinghua University, Min Yuan WeBank, Yu Jiang Tsinghua University
DOI
09:00
20m
Talk
ModelDiff: Testing-Based DNN Similarity Comparison for Model Reuse Detection
Technical Papers
Yuanchun Li Microsoft Research, Ziqi Zhang Peking University, Bingyan Liu Peking University, Ziyue Yang Microsoft Research, Yunxin Liu Tsinghua University
DOI