Frequency Is Devious: Deep Stealthy Backdoor in Artificial Intelligence Based Consumer Internet of Things

Artificial Intelligence (AI) based Consumer Internet of Things (CIoT) flourishes at a rapid speed due to its excellent data collection ability, which plays an important role in optimizing Deep Neural Networks (DNNs) of AI-based CIoT. However, since the CIoT devices can be accessed without the user’s permission, the DNNs of AI-based CIoT face various security threats, especially the backdoor attack caused by data manipulation. Existing backdoor attacks only concentrate on designing imperceptible triggers in the spatial domain, but not the frequency domain, making the triggers easily detectable or even removable by recent defense methods or humans. In this paper, we reveal a DEep STealthy backdoor in DNNs of AI-based CIoT named DEST, which is more invisible in the spatial domain and even imperceptible in the frequency domain. Specifically, we use the singular value decomposition method to fuse the feature components of the trigger into the clean image which is wavelet and fourier transformed at the high frequency components, and use the fourier transform to limit the size of the fused components. We further provide theoretical explanations and literature support for feasibility of the proposed method. Extensive experiments on four datasets and three popular classifiers show that the proposed backdoor attack outperforms other state-of-the-art backdoor methods.