Computing Reviews
Today's Issue Hot Topics Search Browse Recommended My Account Log In
Review Help
Search
DeepXplore: automated whitebox testing of deep learning systems
Pei K., Cao Y., Yang J., Jana S. Communications of the ACM62 (11):137-145,2019.Type:Article
Date Reviewed: Jan 12 2021

Many of us use trustworthy electronic systems, from self-driving car owners to online bankers and shoppers. How should real-life computer systems be methodically tested for nearly all potential faults and malware threats, to instill confidence in users? Pei et al. present DeepXplore, the first system of its kind that uses deep learning (DL) techniques to design and exhaustively test for impending malware threats and defects in software.

The authors identify two major drawbacks of current deep neural network (DNN) testing strategies: (1) the exorbitant human endeavors to create accurate behaviors and classifications for specific chores, and (2) the marginal assessment of various behavioral rules. Consequently, they present DeepXplore, a programmed whitebox archetype for methodically assessing inaccurate situation actions in DNNs, such as self-reliant cars bumping into shield fences.

DeepXplore uses untagged source inputs to create numerous representative neurons for testing the multiplicity of behaviors in DNNs. The unique DeepXplore DL algorithm simultaneously capitalizes on the coverage of neurons and the variety of actions in DNNs to uncover a variety of system faults and failures. The authors present efficient algorithms for resolving the joint optimization problems of neuron coverage and different behaviors in DNNs.

Is DeepXplore effective in exploring threats and failures in emerging online computerized systems? Experiments were performed with datasets that originated from public images, driving, malicious attacks, and different DNNs. The results reveal that neuron coverage is a reliable predictor of DNN testing. But what about issues related to testing simulation shadows, the “efficient search for error-inducing test cases for arbitrary transformations,” and the error-free gradient-based local search used in DeepXplore? I invite colleagues from computational and applied mathematics to immediately investigate these problems and solutions. Clearly, the authors offer compelling, futuristic ideas about the nature of DL and DNN research challenges.

Reviewer:  Amos Olagunju Review #: CR147158 (2105-0126)
Bookmark and Share
  Reviewer Selected
Featured Reviewer
 
 
Learning (I.2.6 )
 
Would you recommend this review?
yes
no
Other reviews under "Learning": Date
Learning in parallel networks: simulating learning in a probabilistic system
Hinton G. (ed) BYTE 10(4): 265-273, 1985. Type: Article
Nov 1 1985
Macro-operators: a weak method for learning
Korf R. Artificial Intelligence 26(1): 35-77, 1985. Type: Article
Feb 1 1986
Inferring (mal) rules from pupils’ protocols
Sleeman D.  Progress in artificial intelligence (, Orsay, France,391985. Type: Proceedings
Dec 1 1985
more...

E-Mail This Printer-Friendly
Send Your Comments
Contact Us
Reproduction in whole or in part without permission is prohibited.   Copyright 1999-2024 ThinkLoud®
Terms of Use
| Privacy Policy