ADVERSARIAL DEFENSE FOR MNIST: INVESTIGATING ADVERSARIAL TRAINING AND FGSM

Kommineni Srivathsav , Sai Manas Rao Pulakonti , Kadali Narayana Anudeep , Kommineni Srinivas , Kommineni Sri Lakshmi Poojitha

This research looks at strategies for defending machine learning models from adversarial assaults, which are deliberate attempts to misclassify input fed to machine learning models in order to trick them. Machine learning systems' dependability and security are seriously threatened by adversarial assaults. The research paper focuses on adversarial training, a popular defense mechanism that involves augmenting the training data with adversarial examples to make the model more robust to adversarial attacks. In the study, a convolutional neural network trained on the MNIST dataset is used as an example to demonstrate how adversarial training might increase the model's performance on adversarial examples. The research paper concludes that adversarial training is an effective defense mechanism but has limitations and should be used in combination with other defense mechanisms. The outcomes show how crucial it is to protect machine learning models against adversarial attacks in order to ensure their dependability and robustness. To create protection systems that are more reliable and effective, further study is required.

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"ADVERSARIAL DEFENSE FOR MNIST: INVESTIGATING ADVERSARIAL TRAINING AND FGSM", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.8, Issue 3, page no.1068 - 1070, March-2023, Available :https://ijsdr.org/papers/IJSDR2303175.pdf

Volume 8 Issue 3, March-2023

Pages : 1068 - 1070

Paper Reg. ID: IJSDR_204792

Published Paper Id: IJSDR2303175

Downloads: 000347459

Research Area: Computer Science & Technology 

Country: Hyderabad, Telangana, India

Published Paper PDF: https://ijsdr.org/papers/IJSDR2303175

Published Paper URL: https://ijsdr.org/viewpaperforall?paper=IJSDR2303175

ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: IJSDR(IJ Publication) Janvi Wave

This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition