FA: A Fast Method to Attack Real-time Object Detection Systems

摘要

With the development of deep learning, image and video processing plays an important role in the age of 5G communication. However, deep neural networks are vulnerable: subtle perturbations can lead to incorrect classification results. Nowadays, adversarial attacks on artificial intelligence models have seen increasing interest. In this study, we propose a new method named FA to generate adversarial examples of object detection models. Based on the generative adversarial network (GAN), we combine the classification and location information to make the generated image look as real as possible. Experimental results on the PASCAL VOC dataset show that our method efficiently and quickly generates the image. Then, we test the transferability of adversarial samples on different datasets and object detection models such as YOLOv4, which also achieve certain transfer performance. Our work provides a basis for further exploring the defects of deep learning and improving the robustness of the systems.