arXiv:2606.12251v1 Announce Type: new Abstract: Gradient-based adversarial attacks remain a dominant threat to deep neural networks (DNNs), as they exploit gradient information to efficiently optimize adversarial perturbations. To address this, we investigate whether reinforcement learning (RL) training can disrupt the gradient structure used by attackers by training image classifiers with policy-gradient objectives and epsilon-greedy exploration. Through systematic experiments across CIFAR-10, CIFAR-100, and ImageNet-100 with multiple architectures, we find that RL-trained classifiers signifi
This research emerges as AI systems become more ubiquitous, increasing the imperative to address their vulnerabilities to adversarial attacks and improve their robustness.
Sophisticated readers should care because this research explores a novel method to enhance AI security by altering the fundamental training mechanisms, potentially leading to more resilient deep learning models.
The conventional understanding that gradient-based attacks are universally dominant may be challenged, introducing new paradigms for adversarial defense and potentially changing the 'red team' tactics for AI security.
- · AI security researchers
- · Organizations deploying critical AI systems
- · Developers of secured AI frameworks
- · Attackers relying solely on gradient-based methods
- · Current adversarial training methodologies
Reinforcement learning-trained AI models could exhibit higher resistance to specific adversarial attacks.
This improved robustness could shift the focus of adversarial research towards non-gradient-based attack vectors or more complex, adaptive defense strategies.
Increased trust in AI systems for critical applications could follow, as their fundamental security against manipulation is enhanced.
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Read at arXiv cs.LG