Author:
Lakhani Archit,Rohit Neyah
Abstract
This paper offers a comprehensive examination of adversarial vulnerabilities in machine learning (ML) models and strategies for mitigating fairness and bias issues. It analyses various adversarial attack vectors encompassing evasion, poisoning, model inversion, exploratory probes, and model stealing, elucidating their potential to compromise model integrity and induce misclassification or information leakage. In response, a range of defence mechanisms including adversarial training, certified defences, feature transformations, and ensemble methods are scrutinized, assessing their effectiveness and limitations in fortifying ML models against adversarial threats. Furthermore, the study explores the nuanced landscape of fairness and bias in ML, addressing societal biases, stereotypes reinforcement, and unfair treatment, proposing mitigation strategies like fairness metrics, bias auditing, de-biasing techniques, and human-in-the-loop approaches to foster fairness, transparency, and ethical AI deployment. This synthesis advocates for interdisciplinary collaboration to build resilient, fair, and trustworthy AI systems amidst the evolving technological paradigm.
Publisher
International Journal of Innovative Science and Research Technology
Reference89 articles.
1. "Adversarial Attacks and Perturbations." Nightfall AI, www.nightfall.ai/ai-security-101/adversarial-attacks-and-perturbations #:~:text=attacks%20and%20perturbations%3F-,Adversarial%20attack s%20and%20perturbations%20are%20techniques%20used%20to%20 exploit%20vulnerabilities,making%20incorrect%20predictions%20or %20decisions. Accessed 4 Jan. 2024.
2. "Adversarial Attacks on Neural Networks: Exploring the Fast Gradient Sign Method." neptune.ai, 24 Aug. 2023, neptune.ai/blog/adversarial-attacks-on-neural-networks-exploring-thefast-gradient-sign-method#:~:text=The%20Fast%20Gradient%20Sign %20Method%20%28FGSM%29%20combines%20a,a%20neural%20 network%20model%20into%20making%20wrong%20predictions. Accessed 4 Jan. 2024.
3. "Know Your Enemy: How You Can Create and Defend against Adversarial Attacks." Medium, 6 Jan. 2019, towardsdatascience.com/know-your-enemy-7f7c5038bdf3. Accessed 4 Jan. 2024.
4. "Data Poisoning: How Machine Learning Gets Corrupted." Roboticsbiz, 11 May 2022, roboticsbiz.com/data-poisoning-how-machine-learning-gets-corrupted /. Accessed 4 Jan. 2024.
5. Zhuo Lv, Hongbo Cao, Feng Zhang, Yuange Ren, Bin Wang, Cen Chen, Nuannuan Li, Hao Chang, Wei Wang, AWFC: Preventing Label Flipping Attacks Towards Federated Learning for Intelligent IoT, The Computer Journal, Volume 65, Issue 11, November 2022, Pages 2849–2859, https://doi.org/10.1093/comjnl/bxac124
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