Genetic Algorithm-Optimized Bit Plane Slicing for Adversarial Attack Resilience in Machine Learning with Efficient Parallel Defense

The goal of this work is to enhance the adversarial defense of machine learning models using a new defense structure based on GA-Optimized Bit Plane Slicing. By breaking data sets into tiers within an information hierarchy, the choice of which planes to select will be optimised in the GA, allowing for minimal adversarial influence while maintaining the calibre of model projections. Several Efficient Parallel Defence principles are also incorporated into the proposed approach. These mechanisms allow you to detect and disrupt hostile activity in real time across many data planes at once. Since this minimises the delay that is associated with standard single-threaded defences it is suited for real world scenarios on which high data throughput is registered. The outcome of all experiments suggests that adversarial robustness is enhanced when using the GA-optimized bit plane slicing framework for a broad variety of datasets and attacking styles. This research provides a scalable and parallelizable defence method that can be used for numerous machine learning applications, it is therefore a contribution to the developing field of secure machine learning.