A Comparative Study on Adversarial Noise Generation for Single Image Classification

Rishabh Saxena, Amit Sanjay Adate and Don Sasikumar
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Rishabh Saxena: VIT University, Vellore, India
Amit Sanjay Adate: VIT University, Vellore, India
Don Sasikumar: VIT University, Vellore, India

International Journal of Intelligent Information Technologies (IJIIT), 2020, vol. 16, issue 1, 75-87

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.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jiit00:v:16:y:2020:i:1:p:75-87

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