Adversarial Robustness Encoder as a Service for Classifiers on Internet of Things Devices

Within the Internet of Things (IoT) landscape, Encoder as a Service (EaaS) is a cloud-based service for many AI empowered scenarios, such as autopilot and face-scan payment, enabling IoT devices to keep a light classifier model at local while remotely accessing powerful encoding models. However, adversarial examples like an image with imperceptible tiny perturbations that can lead to incorrect classifying results, make it challenging to achieve a robust EaaS-based classifier. Certified radius (R) emerges as a measure against such imperceptible tiny perturbations, and guarantees the trustworthiness of any input with perturbations smaller than R. Despite offering R related services, the substantial computational overhead from traditional methods, stemming from the extensive searches of R for each request, poses a barrier to their practical deployment. In this article, we identify the repetitive computations in the EaaS framework due to the fixed encoding model and propose a novel search cache scheme to speed up the R-related computation. Therefore, we propose large-scale efficient robust EaaS (ScaleRES) to address different R-related services: First, ScaleRES strategically stores previous computation results of R, to enable the reuse and refining of R across users. Then, ScaleRES obtains the average certified radius (ACR) efficiently with selective cached R. Finally, ScaleRES performs R filtering to enhance adversarial training for a robust EaaS-based classifier. Comprehensive evaluations demonstrate that in three distinct computations, ScaleRES offers significant savings in computational overhead compared to the conventional approach—70% for R computation as the number of clients increases, 35% for ACR of the entire test set, and 40% for adversarial training with robustness comparable to other works.