Protecting the Visual Fidelity of Machine Learning Datasets Using QR Codes

Yang Wai Chow, Willy Susilo, Jianfeng Wang, Richard Buckland, Joonsang Baek, Jongkil Kim, Nan Li

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

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.

Original languageEnglish
Title of host publicationMachine Learning for Cyber Security - 2nd International Conference, ML4CS 2019, Proceedings
EditorsXiaofeng Chen, Xinyi Huang, Jun Zhang
PublisherSpringer Verlag
Pages320-335
Number of pages16
ISBN (Print)9783030306182
DOIs
StatePublished - 2019
Event2nd International Conference on Machine Learning for Cyber Security, ML4CS 2019 - Xi'an, China
Duration: 19 Sep 201921 Sep 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11806 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference2nd International Conference on Machine Learning for Cyber Security, ML4CS 2019
Country/TerritoryChina
CityXi'an
Period19/09/1921/09/19

Bibliographical note

Publisher Copyright:
© 2019, Springer Nature Switzerland AG.

Keywords

  • Adversarial machine learning
  • Cyber security
  • QR code
  • Visual fidelity
  • Watermarking

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