报告人：Xiyue Zhang (Peking University)
时间：2020-11-13 12:00-13:30
地点：Room 1303, Sciences Building No. 1
各位数院研究生同学：
研究生学术午餐会是在学院领导的大力支持下，由研究生会负责组织的系列学术交流活动。午餐会每次邀请一位同学作为主讲人，面向全院各专业背景的研究生介绍自己科研方向的基本问题、概念和方法，并汇报近期的研究成果和进展，是研究生展示自我、促进交流的学术平台。
研究生会已经举办了四十三期活动，我们将于2020年11月13日周五举办第四十五期学术午餐会活动，欢迎感兴趣的老师和同学积极参加。
报告人简介：张喜悦，欧冠联赛直播免费观看2017级直博研究生，导师为孙猛教授。研究方向为程序理论、软件形式化方法，研究工作包括针对区块链智能合约、深度学习系统、黑盒系统的建模、验证和测试。曾获2020国家奖学金，2019-2020（欧冠联赛直播免费观看）校长奖学金。
报告摘要[Abstract]：Over the past decade, deep learning (DL) has been successfully applied to many industrial domain-specific tasks. However, the current state-of-the-art DL software still suffers from quality issues, which raises great concern especially in the context of safety- and security-critical scenarios. Adversarial examples (AEs) represent a typical and important type of defects needed to be urgently addressed, on which a DL software makes incorrect decisions. Such defects occur through either intentional attack or physical-world noise perceived by input sensors, potentially hindering further industry deployment. The intrinsic uncertainty nature of deep learning decisions can be a fundamental reason for its incorrect behavior. Although some testing, adversarial attack and defense techniques have been recently proposed, it still lacks a systematic study to uncover the relationship between AEs and DL uncertainty.
In this paper, we conduct a large-scale study towards bridging this gap. We first investigate the capability of multiple uncertainty metrics in differentiating benign examples (BEs) and AEs, which enables to characterize the uncertainty patterns of input data. Then, we identify and categorize the uncertainty patterns of BEs and AEs, and find that while BEs and AEs generated by existing methods do follow common uncertainty patterns, some other uncertainty patterns are largely missed. Based on this, we propose an automated testing technique to generate multiple types of uncommon AEs and BEs that are largely missed by existing techniques. Our further evaluation reveals that the uncommon data generated by our method is hard to be defended by the existing defense techniques with the average defense success rate reduced by 35%. Our results call for attention and necessity to generate more diverse data for evaluating quality assurance solutions of DL software.
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