日本データベース学会の皆様 国立情報学研究所の片山です。 講演会のご案内です。 ジョージア工科大学コンピュータサイエンス学部の教授であるLing Liu氏の講演 会「Robustness of Deep Learning Systems Against Deception 」を7月18日に 開催致します。詳細は以下の通りです。 Date: July 18 (Thursday) Time: 17:00-18:00 Venue: National Institute of Informatics 12F (conference room #1208) 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo https://www.nii.ac.jp/en/about/access/ Ling Liu教授は、同大学分散データ集中システム研究所で研究プログラムを指揮 しており、300以上の国際的なジャーナルと会議の記事を発表するとともに、 ICDCS 2003、WWW 2004、2005 Pat Goldberg Memorial Best Paper Award、IEEE Cloud 2012、IEEE ICWS 2013、IEEE / ACM CCGrid 2015、IEEE Edge 2017など、 多くのトップカンファレンスにて最優秀論文賞を受賞されています。 Ling Liu教授のご講演を聴く貴重な機会ですので、是非ご参加ください。 なお、講演はすべて英語で行われます（通訳はありません）。参加費は無料です。 席に限りがあるようですので、参加される方は、以下の「参加申し込み」より 事前登録をお願い致します。よろしくお願い致します。 https://www.nii.ac.jp/event/2019/0718.html <Title of talk> Robustness of Deep Learning Systems Against Deception by Ling Liu, Professor in the School of Computer Science at Georgia Institute of Technology <Abstract> We are entering an exciting era where human intelligence is being enhanced by big data fueled artificial intelligence (AI) and machine learning (ML). However, recent work shows that DNN models trained privately are vulnerable to adversarial inputs. Such adversarial inputs inject small amount of perturbations to the input data to fool machine learning models to misbehave, turning a deep neural network against itself. As new defense methods are proposed, more sophisticated attack algorithms are surfaced. This arms race has been ongoing since the rise of adversarial machine learning. This talk provides a comprehensive analysis and characterization of the state of art attacks and defenses. As more mission critical systems are incorporating machine learning and AI as an essential component in our social, cyber, and physical systems, such as Internet of things, self-driving cars, smart planets, smart manufacturing, understanding and ensuring the verifiable robustness of deep learning becomes a pressing challenge. This includes (1) the development of formal metrics to quantitatively evaluate and measure the robustness of a DNN prediction with respect of intentional and unintentional artifacts and deceptions, (2) the comprehensive understanding of the blind spots and the invariants in the DNN trained models and the DNN training process, and (3) the statistical measurement of trust and distrust that we can place on a deep learning algorithm to perform reliably and truthfully. In this talk, I will use our cross-layer strategic teaming defense framework and techniques to illustrate the feasibility of ensuring robust deep learning through scenario-based empirical analysis. <Short bio of Prof. Ling Liu> Ling Liu, Professor in the School of Computer Science at Georgia Institute of Technology Prof. Dr. Ling Liu is a Professor in the School of Computer Science at Georgia Institute of Technology. She directs the research programs in Distributed Data Intensive Systems Lab (DiSL), examining various aspects of large-scale data intensive systems. Prof. Liu is an internationally recognized expert in the areas of Big Data Systems and Analytics, Distributed Systems, Database and Storage Systems, Internet Computing, Privacy, Security and Trust. Prof. Liu has published over 300 international journal and conference articles, and is a recipient of the best paper award from a number of top venues, including ICDCS 2003, WWW 2004, 2005 Pat Goldberg Memorial Best Paper Award, IEEE CLOUD 2012, IEEE ICWS 2013, ACM/IEEE CCGrid 2015, IEEE Edge 2017. Prof. Liu is an elected IEEE Fellow and a recipient of IEEE Computer Society Technical Achievement Award. Prof. Liu has served as general chair and PC chairs of numerous IEEE and ACM conferences in the fields of big data, cloud computing, data engineering, distributed computing, very large databases, World Wide Web, and served as the editor in chief of IEEE Transactions on Services Computing from 2013-2016. Currently Prof. Liu is co-PC chair of The Web 2019 (WWW 2019) and the Editor in Chief of ACM Transactions on Internet Technology (TOIT). Prof. Liu's research is primarily sponsored by NSF, IBM and Intel.