Toward Secure and Efficient Deep Learning Inference in Dependable IoT Systems

Abstract

The rapid development of deep learning (DL) enables resource-constrained systems and devices [e.g., Internet of Things (IoT)] to perform sophisticated artificial intelligence (AI) applications. However, AI models, such as deep neural networks (DNNs), are known to be vulnerable to adversarial examples (AEs). Past works on defending against AEs require heavy computations in the model training or inference processes, making them impractical to be applied in IoT systems. In this article, we propose a novel method, Super-IoT, to enhance the security and efficiency of AI applications in distributed IoT systems. Specifically, Super-IoT utilizes a pixel drop operation to eliminate adversarial perturbations from the input and reduce network transmission throughput. Then, it adopts a sparse signal recovery method to reconstruct the dropped pixels and wavelet-based denoising method to reduce the artificial noise. Super-IoT is a lightweight method with negligible computation cost to IoT devices and little impact on the DNN model performance. Extensive evaluations show that it can outperform three existing AE defensive solutions against most of the AE attacks with better transmission efficiency.