The increasing prevalence of adversarial attacks on Artificial Intelligence (AI) systems has created a need for innovative security measures. However, the current methods of defending against these attacks often come with a high computing cost and require back-end processing, making real-time defense challenging. Fortunately, there have been remarkable advancements in edge-computing, which make it easier to deploy neural networks on edge devices. Building upon these advancements, we propose an edge framework design to enable universal and efficient detection of adversarial attacks. This framework incorporates an attention-based adversarial detection methodology and a lightweight detection network formation, making it suitable for a wide range of neural networks and can be deployed on edge devices. To assess the effectiveness of our proposed framework, we conducted evaluations on five neural networks. The results indicate an impressive 97.43% F-score can be achieved, demonstrating the framework's proficiency in detecting adversarial attacks. Moreover, our proposed framework also exhibits significantly reduced computing complexity and cost in comparison to previous detection methods. This aspect is particularly beneficial as it ensures that the defense mechanism can be efficiently implemented in real-time on-edge devices.

翻译：人工智能系统面临的对抗攻击日益普遍，这催生了对创新性安全措施的需求。然而，当前的防御方法通常计算成本高昂且需要后端处理，使得实时防御颇具挑战。幸运的是，边缘计算领域已取得显著进展，使得在边缘设备上部署神经网络变得更加容易。基于这些进展，我们提出了一种边缘框架设计，以实现通用且高效的对抗攻击检测。该框架融合了一种基于注意力的对抗检测方法和一种轻量级的检测网络结构，使其适用于广泛的神经网络，并能够部署在边缘设备上。为评估所提框架的有效性，我们在五个神经网络上进行了评估。结果表明，该框架可以达到97.43%的F分数，证明了其在检测对抗攻击方面的卓越能力。此外，与先前的检测方法相比，我们提出的框架还显著降低了计算复杂度和成本。这一特性尤其有益，因为它确保了防御机制能够在边缘设备上高效地实时实现。