Abstract
Artificial intelligence (AI) has been widely adopted in various applications such as face detection, speech recognition, machine learning, etc. Due to the lack of theoretical explanation, recent works show that AI is vulnerable to adversarial attacks, especially deep neural networks could be easily fooled by adversarial examples that are in the form of subtle perturbations to the inputs. The intrinsic vulnerability of AI might incur severe security problems in areas like automatic driving, face payment, voice command control, etc. Adversarial learning is one typical defense method, which can migrate such security risk of AI by training with generated adversarial examples. However, this method cannot defend the growing number of adversarial attacks. The special section of “Artificial Intelligence Security: Adversarial Attack and Defense” focused on the state-of-art adversarial attack and defense methods, and explored how these security problems could affect other areas such as cyberspace security, and internet-of-things.
Sign-in/Register to access full text options 
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 Network Science and Engineering
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.
