IADT: Interpretability-analysis-based Differential Testing for Deep Neural Network
Author:
Affiliation:

Clc Number:

TP311

Fund Project:

  • Article
  • |
  • Figures
  • |
  • Metrics
  • |
  • Reference
  • |
  • Related
  • |
  • Cited by
  • |
  • Materials
  • |
  • Comments
    Abstract:

    With the rapid development of deep neural network (DNN), the accuracy of DNN has become comparable to or even surpassed that of humans in some specific tasks. However, like traditional software, DNN is inevitably prone to defects. If defective DNN models are applied to safety-critical fields, they may cause serious accidents. Therefore, it is urgent to propose effective methods to detect defective DNN models. The traditional differential testing methods rely on the output of the testing target at the same test input as the basis for difference analysis. However, even different DNN models trained with the same program and dataset may produce different outputs under the same test input. Therefore, it is difficult to directly use the traditional differential testing method for detecting defective DNN models. To solve the above problems, this study proposes interpretation-analysis-based differential testing (IADT), an interpretation-analysis-based differential testing method for DNN models. IADT uses interpretation methods to analyze the behavior explanation of DNN models and uses statistical methods to analyze the significant differences in the models’ behavior interpretations to detect defective models. Experiments carried out on real defective models show that the introduction of interpretation methods makes IADT effective in detecting defective DNN models, while the F1-value of IADT in detecting defective models is 0.8% –6.4% greater than that of DeepCrime, and the time consumed by IADT is only 4.0%–5.4% of DeepCrime.

    Reference
    Related
    Cited by
Get Citation

谢瑞麟,崔展齐,陈翔,李莉. IADT: 基于解释分析的深度神经网络差分测试.软件学报,2024,35(12):5452-5469

Copy
Share
Article Metrics
  • Abstract:
  • PDF:
  • HTML:
  • Cited by:
History
  • Received:March 17,2023
  • Revised:June 09,2023
  • Adopted:
  • Online: August 14,2024
  • Published: December 06,2024
You are the firstVisitors
Copyright: Institute of Software, Chinese Academy of Sciences Beijing ICP No. 05046678-4
Address:4# South Fourth Street, Zhong Guan Cun, Beijing 100190,Postal Code:100190
Phone:010-62562563 Fax:010-62562533 Email:jos@iscas.ac.cn
Technical Support:Beijing Qinyun Technology Development Co., Ltd.

Beijing Public Network Security No. 11040202500063