Physical adversarial attack in artificial intelligence of things

Abstract

With the continuous development of wireless communication and artificial intelligence technology, Internet of Things (IoT) technology has made great progress. Deep learning methods are currently used in IoT technology, but deep neural networks (DNNs) are notoriously susceptible to adversarial examples, and subtle pixel changes to images can result in incorrect recognition results from DNNs. In the real-world application, the patches generated by the recent physical attack methods are larger or less realistic and easily detectable. To address this problem, a Generative Adversarial Network based on Visual attention model and Style transfer network (GAN-VS) is proposed, which reduces the patch area and makes the patch more natural and less noticeable. A visual attention model combined with generative adversarial network is introduced to detect the critical regions of image recognition, and only generate patches within the critical regions to reduce patch area and improve attack efficiency. For any type of seed patch, an adversarial patch can be generated with a high degree of stylistic and content similarity to the attacked image by generative adversarial network and style transfer network. Experimental evaluation shows that the proposed GAN-VS has good camouflage and outperforms state-of-the-art adversarial patch attack methods.