Publication Type : Journal Article
Publisher : International Journal of Intelligent Information Technologies
Source : International Journal of Intelligent Information Technologies, Volume 16, Issue 1 (2020)
Campus : Amritapuri
School : Department of Computer Science and Engineering, School of Engineering
Center : Computer Vision and Robotics
Department : Computer Science
Year : 2020
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.
Cite this Research Publication : Dr. Don S., Rishabh Saxena, and Amit Sanjay Adate, “A Comparative Study on Adversarial Noise Generation for Single Image Classification”, International Journal of Intelligent Information Technologies, vol. 16, no. 1, 2020.