Conditional Generative Adversarial Networks with Adversarial Attack and Defense for Generative Data Augmentation

Francis Baek, Daeho Kim, Somin Park, Hyoungkwan Kim, Sanghyun Lee

Research output: Contribution to journalArticlepeer-review

Abstract

Developing deep neural network (DNN) models for computer vision applications for construction is challenging due to the shortage of training data. To address this issue, we proposed a novel data augmentation method that integrates a conditional generative adversarial networks (GANs) framework with a target classifier. The integrated architecture enables adversarial attack and defense during end-to-end training, thereby making it possible to generate effective images for the target classifier's training. We trained and tested two image classification DNNs with and without data augmentation, where we confirmed the effectiveness of the proposed method: with the data augmentation, the classification accuracy improved by 4.2 percentage points, from 71.24% to 75.46%, with qualitatively improved feature extraction more focused on the target object. Given that the application areas of our method are open-ended, the result is noteworthy. The proposed method can help construction researchers offset the data insufficiency, which will contribute to having more accurate and scalable DNN-powered vision models in construction applications.

Original languageEnglish
Article number04022001
JournalJournal of Computing in Civil Engineering
Volume36
Issue number3
DOIs
Publication statusPublished - 2022

Bibliographical note

Publisher Copyright:
© 2022 American Society of Civil Engineers.

All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Computer Science Applications

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