F.2.7. Optimization
alireza Omidi nasab; Sajad Bastami; Rojiar Pir Mohammadiani; Mohammad Bagher Dowlatshahi; Seyedeh Zahra Mousavi
Abstract
Deep Neural Networks (DNNs) are increasingly deployed in safety-critical domains such as autonomous driving, healthcare, finance, and natural language processing, yet they remain vulnerable to adversarial attacks—subtle manipulations that can cause confident misclassifications or misleading predictions. ...
Read More
Deep Neural Networks (DNNs) are increasingly deployed in safety-critical domains such as autonomous driving, healthcare, finance, and natural language processing, yet they remain vulnerable to adversarial attacks—subtle manipulations that can cause confident misclassifications or misleading predictions. This fragility poses a major barrier to building secure and trustworthy AI systems. Conventional defenses, including adversarial training and heuristic detection, often struggle to balance robustness, adaptability, and computational cost. To overcome these limitations, we propose a hybrid adaptive defense framework that unifies Ant Colony Optimization (ACO) with Reinforcement Learning (RL). ACO efficiently explores the high-dimensional space of defense hyperparameters to find globally optimal configurations, while RL enables dynamic, context-aware adaptation of defense strategies in real time. The proposed ACO-RL framework was rigorously evaluated across six diverse benchmark datasets spanning multiple data modalities: MNIST and CIFAR-10 (vision), IMDB and AG News (text), and Cora and Reddit-Binary (graph). Experimental results show that ACO-RL consistently enhances robustness against a wide spectrum of adversarial attacks, outperforming several state-of-the-art baselines. These findings highlight a promising pathway toward developing resilient, cross-domain AI systems capable of defending against evolving adversarial threats.
