Enhancing Machine Learning Resilience to Adversarial Attacks through Bit Plane Slicing Optimized by Genetic Algorithms
Keywords:
Adversarial attacks; Bit plane slicing; Defense strategies; Machine learning security; Genetic AlgorithmAbstract
This research delves into enhancing the resilience of machine learning models, particularly image classification algorithms, against adversarial attacks. The focus is on using genetic algorithms to optimize bit plane slicing configurations, thereby improving the models’ robustness. The study reveals that models with 5-bit depth representations exhibit superior resilience, achieving high accuracies of against FGSM attacks and against DeepFool attacks. These results underscore the importance of adjusting detail levels through bit plane slicing to main
This research delves into enhancing the resilience of machine learning models, particularly image classification algorithms, against adversarial attacks. The focus is on using genetic algorithms to optimize bit plane slicing configurations, thereby improving the models’ robustness. The study reveals that models with 5-bit depth representations exhibit superior resilience, achieving high accuracies of against FGSM attacks and against DeepFool attacks. These results underscore the importance of adjusting detail levels through bit plane slicing to maintain algorithmic integrity under adversarial conditions. Despite a significant drop in performance due to adversarial modifications, with accuracy falling from to , a notable recovery was observed, highlighting the effectiveness of the optimized defense strategies. The findings advocate for further research into dynamic bit plane slicing and the development of advanced defense mechanisms using genetic algorithms, aiming to bolster the security and reliability of machine learning models against the continuously evolving adversarial threats.
tain algorithmic integrity under adversarial conditions. Despite a significant drop in performance due to adversarial modifications, with accuracy falling from to , a notable recovery was observed, highlighting the effectiveness of the optimized defense strategies. The findings advocate for further research into dynamic bit plane slicing and the development of advanced defense mechanisms using genetic algorithms, aiming to bolster the security and reliability of machine learning models against the continuously evolving adversarial threats.
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