Author(s): P. Sathish Kumar, K.V.D. Kiran

Email(s): pandaramsathishkumar@gmail.com

DOI: 10.52711/2321-581X.2023.00002   

Address: P. Sathish Kumar1, K.V.D. Kiran2
1Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, AP, India.
2Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, AP, India.
*Corresponding Author

Published In:   Volume - 14,      Issue - 1,     Year - 2023


ABSTRACT:
Deep neural networks (DNNs) are particularly vulnerable to adversarial samples when used as machine learning (ML) models. These kinds of samples are typically created by combining real-world samples with low-level sounds so they can mimic and deceive the target models. Since adversarial samples may switch between many models, black-box type attacks can be used in a variety of real-world scenarios. The main goal of this project is to produce an adversarial assault (white box) using PyTorch and then offer a defense strategy as a countermeasure. We developed a powerful offensive strategy known as the MI-FGSM (Momentum Iterative Fast Gradient Sign Method). It can perform better than the I-FGSM because to its adaptation (Iterative Fast Gradient Sign Method). The usage of MI-FGSM will greatly enhance transferability. The other objective of this project is to combine machine learning algorithms with quantum annealing solvers for the execution of adversarial attack and defense. Here, we'll take model-based actions based on the existence of attacks. Finally, we provide the experimental findings to show the validity of the developed attacking method by assessing the strengths of various models as well as the defensive strategies.


Cite this article:
P. Sathish Kumar, K.V.D. Kiran. Momentum Iterative Fast Gradient Sign Algorithm for Adversarial Attacks and Defenses. Research Journal of Engineering and Technology. 2023; 14(1):7-4. doi: 10.52711/2321-581X.2023.00002

Cite(Electronic):
P. Sathish Kumar, K.V.D. Kiran. Momentum Iterative Fast Gradient Sign Algorithm for Adversarial Attacks and Defenses. Research Journal of Engineering and Technology. 2023; 14(1):7-4. doi: 10.52711/2321-581X.2023.00002   Available on: https://www.ijersonline.org/AbstractView.aspx?PID=2023-14-1-2


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