[image 03613] NII講演会:Prof. Ling Liu, Robustness of Deep Learning Systems Against Deception

Isao Echizen iechizen @ nii.ac.jp
2019年 7月 3日 (水) 10:12:55 JST


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

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

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
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、

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。

<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

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.

Isao Echizen, Ph.D
Deputy Director General, National Institute of Informatics (NII)
Professor, Information and Society Research Division,
National Institute of Informatics (NII)
Professor, Department of Information and Communication Engineering,
Graduate School of Information Science and Technology, The University of
2-1-2, Hitotsubashi, Chiyoda-ku, Tokyo, 101-8430, JAPAN
mailto: iechizen @ nii.ac.jp
TEL:+81-3-4212-2516 (Direct), FAX:+81-3-3556-1916

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