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ISSTA 2021
Sun 11 - Sat 17 July 2021 Online
co-located with ECOOP and ISSTA 2021

Deep learning (DL) systems are increasingly deployed for autonomous decision-making in a wide range of applications. Apart from the robustness and safety, fairness is also an important property that a well-designed DL system should have. To evaluate and improve individual fairness of a model, systematic test case generation for identifying individual discriminatory instances in the input space is essential. In this paper, we propose a framework EIDIG for efficiently discovering individual fairness violation. Our technique combines a global generation phase for rapidly generating a set of diverse discriminatory seeds with a local generation phase for generating as many individual discriminatory instances as possible around these seeds under the guidance of the gradient of the model output. In each phase, prior information at successive iterations is fully exploited to accelerate convergence of iterative optimization or reduce frequency of gradient calculation. Our experimental results show that, on average, our approach EIDIG generates 19.11% more individual discriminatory instances with a speedup of 121.49% when compared with the state-of-the-art method and mitigates individual discrimination by 80.03% with a limited accuracy loss after retraining.

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