ExAL: An Exploration Enhanced Adversarial Learning Algorithm
This addresses the challenge of improving defense against adversarial attacks for machine learning systems, though it appears incremental as it builds on existing adversarial learning frameworks.
The paper tackles the problem of limited model resilience in adversarial learning by proposing ExAL, an exploration-enhanced algorithm that uses EMPSO to generate optimized adversarial perturbations. Experimental results on MNIST and Blended Malware datasets show it significantly enhances model robustness against attacks.
Adversarial learning is critical for enhancing model robustness, aiming to defend against adversarial attacks that jeopardize machine learning systems. Traditional methods often lack efficient mechanisms to explore diverse adversarial perturbations, leading to limited model resilience. Inspired by game-theoretic principles, where adversarial dynamics are analyzed through frameworks like Nash equilibrium, exploration mechanisms in such setups allow for the discovery of diverse strategies, enhancing system robustness. However, existing adversarial learning methods often fail to incorporate structured exploration effectively, reducing their ability to improve model defense comprehensively. To address these challenges, we propose a novel Exploration-enhanced Adversarial Learning Algorithm (ExAL), leveraging the Exponentially Weighted Momentum Particle Swarm Optimizer (EMPSO) to generate optimized adversarial perturbations. ExAL integrates exploration-driven mechanisms to discover perturbations that maximize impact on the model's decision boundary while preserving structural coherence in the data. We evaluate the performance of ExAL on the MNIST Handwritten Digits and Blended Malware datasets. Experimental results demonstrate that ExAL significantly enhances model resilience to adversarial attacks by improving robustness through adversarial learning.