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【信息学院学术报告】Deep Neural Network and Artificial Intelligent Security

[发表时间]:2018-06-13 [来源]: [浏览次数]:

讲座时间:  2018613日(周三)下午14 :00

讲座地点:  沙河校区 7号楼 216  

: 金意儿  

讲座语言:  中文

讲座摘要:

With the rapid growth and significant successes in a wide spectrum of applications, Deep Learning (DL) has been applied in many real-world applications including those safety-critical scenarios. However, the increasing popularity also comes with new security concerns to deep learning utilization. Specifically, Deep Neural Networks (DNN) are highly vulnerable to adversarial examples, which can easily fool the DNN to produce misclassification errors with high confidence. In this talk, I will first introduce previous methods for generating adversarial examples which focus mainly on adding perturbation to input images directly. Orthogonal to existing solutions, I will then present our research effort and proof-of-concept implementation of adversarial feature manipulation attacks against deep learning applications. Rather than concentrating on modifying input vectors of DNN, we craft adversarial examples based on the precise understanding of the sensitivity between max-pooling feature representation and final classification output. The emerging hardware-software DNN framework will also be introduced to help better understand the security vulnerabilities of the DNN systems.

主讲人简介:

Yier Jin is the Endowed IoT Term Professor in the Warren B. Nelms Institute for the Connected World and also an Associate Professor in the Department of Electrical and Computer Engineering (ECE) in the University of Florida (UF). Prior to joining UF, he was an assistant professor in the ECE Department at the University of Central Florida (UCF). He received his PhD degree in Electrical Engineering in 2012 from Yale University after he got the B.S. and M.S. degrees in Electrical Engineering from Zhejiang University, China, in 2005 and 2007, respectively. His research focuses on the areas of embedded systems design and security, trusted hardware intellectual property (IP) cores and hardware-software co-design for modern computing systems. His is currently focusing on the design and security analysis on Internet of Things (IoT) and wearable devices with particular emphasis on information integrity and privacy protection in the IoT era. Dr. Jin received Department of Energy (DoE) early CAREER award in 2016 and the Outstanding New Faculty Award of ACM's Special Interest Group on Design Automation (SIGDA) in 2017. He also received the Best Paper Award of the 52nd Design Automation Conference in 2015, the 21st Asia and South Pacific Design Automation Conference in 2016, the 10th IEEE Symposium on Hardware-Oriented Security and Trust in 2017, the 2018 ACM TODAES, and the 28th edition of the ACM Great Lakes Symposium on VLSI.

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