Style transfer based adversarial patches for object detection model attacks

Physical attacks on object detection models can be carried out using adversarial patches generated through deep learning. If the object is applied with the adversarial patch, the detector cannot find the object in the image. However, the patch generated by the traditional method often looks obvious and strange, making it very conspicuous in the physical world and hard to attack the object detection model covertly. In this paper, we propose a new method that combines style transfer with traditional patch generation to enhance the semantic content of images. Using style transfer, we can change an image's style while preserving its original character. We try different style transfer algorithms and compare each patch's attack performance, finding that using the convolutional neural networks based algorithm on the patch can make the patch's attack performance reserve the most.