Abstract:
The study sought to enhance the resilience of deep learning models against
adversarial attacks, particularly those leveraging the Fast Gradient Sign Method
(FGSM), which presents a significant vulnerability in the security of machine learning
systems. Amidst growing concerns over the effectiveness of conventional defense
mechanisms, the specific objectives were to investigate existing models' robustness,
implement a novel block-switching algorithm for defense, and evaluate its performance
metrics in safeguarding against adversarial threats. Employing an empirical research
methodology, the study utilized a combination of widely recognized deep learning
architectures (ResNet, VGG, and Inception) pre-trained on the ImageNet dataset. This
involved generating adversarial examples using FGSM to assess the models' resilience
and conducting a sensitivity analysis on the impact of attack parameters on model
performance. The study found that traditional defense strategies often fell short when
subjected to sophisticated FGSM attacks, underlining the necessity for innovative
solutions like the block-switching algorithm. Further, the findings established that the
block-switching algorithm significantly improved model robustness by dynamically
altering model components in response to detected threats, thereby enhancing defense
capabilities against a variety of adversarial attacks. The study concludes that the blockswitching algorithm represents a promising advancement in the field of adversarial
defense, offering a more adaptable and effective approach to securing deep learning
models. It recommends further research into adaptive defense mechanisms and the
exploration of block-switching algorithm applications across different deep learning
architectures and attack vectors. These findings will have a profound impact on the
development of more secure Artificial Intelligence (AI) systems, influencing future
cybersecurity strategies and contributing to the establishment of resilient machine
learning frameworks capable of withstanding the evolving landscape of cyber threats.
