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A Comparative Study on Adversarial Noise Generation for Single Image Classification

Author

Listed:
  • Rishabh Saxena

    (VIT University, Vellore, India)

  • Amit Sanjay Adate

    (VIT University, Vellore, India)

  • Don Sasikumar

    (VIT University, Vellore, India)

Abstract
With the rise of neural network-based classifiers, it is evident that these algorithms are here to stay. Even though various algorithms have been developed, these classifiers still remain vulnerable to misclassification attacks. This article outlines a new noise layer attack based on adversarial learning and compares the proposed method to other such attacking methodologies like Fast Gradient Sign Method, Jacobian-Based Saliency Map Algorithm and DeepFool. This work deals with comparing these algorithms for the use case of single image classification and provides a detailed analysis of how each algorithm compares to each other.

Suggested Citation

  • Rishabh Saxena & Amit Sanjay Adate & Don Sasikumar, 2020. "A Comparative Study on Adversarial Noise Generation for Single Image Classification," International Journal of Intelligent Information Technologies (IJIIT), IGI Global, vol. 16(1), pages 75-87, January.
    • Handle: RePEc:igg:jiit00:v:16:y:2020:i:1:p:75-87
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    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJIIT.2020010105
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