Abstract
The study reveals that both CNNs and SVMs are significantly sensitive to
adversarial manipulation, leading to drastic reductions in accuracy. Through
the application of adversarial training, the study identifies a trade-off between
enhancing robustness against adversarial examples and maintaining
performance on clean data. The findings emphasise the importance of optimizing
model architectures, hyperparameters, and training strategies to mitigate
vulnerabilities. Recommendations for future studies include developing
more sophisticated adversarial training methods, exploring alternative
machine learning models, and tailoring defence strategies to specific realworld
applications. This study contributes to the field of adversarial machine
learning by providing empirical evidence and insights that inform the
development of more secure and reliable machine learning systems.