Backdoor Attack on Deep Neural Networks Triggered by Fault Injection Attack on Image Sensor Interface
Tatsuya Oyama, Shunsuke Okura, Kota Yoshida, Takeshi Fujino
Abstract
Recent automobiles use image sensors to take in the physical world information, and deep neural networks (DNNs) are used to recognize the surroundings to control the vehicles. Adversarial examples and backdoor attacks that induce misclassification by tampering with input images to DNN have been proposed as methods of attacking DNNs. As an example of attacks on DNNs equipped in automobiles, a method has been reported in which an adversarial mark is added to input images by physically putting a sticker on a road sign. However, the method reduces reproducibility due to the influence of the shooting environment. The tampering area needs to be increased to improve reproducibility. However, these large marks are easily seen by people.