Abstract
With appearing of adversarial examples which are able to fool deep neural networks, some defense methods are developed to recognize adversarial images. This paper focuses on a new kind of detection on adversarial examples, which aims to find the abnormal user who utilized adversarial examples among a number of normal users. Due to the utilization of adversarial images, the abnormal user is significantly deviated from the majority normal users. Based on this, we propose a straightforward method to extract abnormity information, and design a pooled detection scheme to identify the abnormal user by hierarchical clustering. Experimental results show that our scheme is able to identify the utilization of popular adversarial example methods, and achieve low computational complexity.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: IEEE Transactions on Emerging Topics in Computational Intelligence
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
