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Protecting the Visual Fidelity of Machine Learning Datasets Using QR Codes

  • Yang-Wai ChowEmail author
  • Willy Susilo
  • Jianfeng Wang
  • Richard Buckland
  • Joonsang Baek
  • Jongkil Kim
  • Nan Li
Conference paper
  • 610 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11806)

Abstract

Machine learning is becoming increasingly popular in a variety of modern technology. However, research has demonstrated that machine learning models are vulnerable to adversarial examples in their inputs. Potential attacks include poisoning datasets by perturbing input samples to mislead a machine learning model into producing undesirable results. Such perturbations are often subtle and imperceptible from a human’s perspective. This paper investigates two methods of verifying the visual fidelity of image based datasets by detecting perturbations made to the data using QR codes. In the first method, a verification string is stored for each image in a dataset. These verification strings can be used to determine whether an image in the dataset has been perturbed. In the second method, only a single verification string stored and is used to verify whether an entire dataset is intact.

Keywords

Adversarial machine learning Cyber security QR code Visual fidelity Watermarking 

Notes

Acknowledgment

The authors would like to acknowledge the support of the NSW Cybersecurity Network grant, the NUW Alliance grant and the National Natural Science Foundation of China (Nos. 61572382 and 61702401) that were awarded for this research.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Yang-Wai Chow
    • 1
    Email author
  • Willy Susilo
    • 1
  • Jianfeng Wang
    • 2
  • Richard Buckland
    • 3
  • Joonsang Baek
    • 1
  • Jongkil Kim
    • 1
  • Nan Li
    • 4
  1. 1.Institute of Cybersecurity and Cryptology, School of Computing and Information TechnologyUniversity of WollongongWollongongAustralia
  2. 2.State Key Laboratory of Integrated Service Networks (ISN)Xidian UniversityXidianChina
  3. 3.School of Computer Science and EngineeringUniversity of New South WalesSydneyAustralia
  4. 4.School of Electrical Engineering and ComputingUniversity of NewcastleNewcastleAustralia

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