Full-coverage Invisible Camouflage For Adversarial Targeted Attack
Hongtuo Zhou, Zhichao Lian · 2023
With the rapid advancement of artificial intelligence technology in recent years, there has been an increasing focus on security issues. Deep learning models, despite their capabilities, are susceptible to various attacks such as counter samples, patches, and camouflage, which can lead to inaccurate outputs. However, existing physical attack methods often overlook the importance of environmental invisibility, making it easier to detect camouflaged objects. In this study, we propose a novel method for generating targeted covert camouflage to effectively attack detection models while remaining undetected. Specifically, our method incorporates targeted attack loss into the yolov3 detection model and combines style transfer and three-dimensional camouflage training to seamlessly integrate camouflage with the surrounding environment. This approach significantly enhances the camouflage’s ability to blend into the environment. Through extensive experiments, we have demonstrated the effectiveness of our targeted covert camouflage method, which outperforms other existing approaches in terms of concealment.