AIMCOM2 Workshop Riding with AI towards

Transcript:AIMCOM2 Workshop Riding with AI towards:
AIMCOM2 Workshop Riding with AI towards Mission-Critical Communications and Computing at the Edge Session D The 28th IEEE International Conference on Network Protocols (ICNP 2020) Madrid, Spain, October 13, 2020 AIMCOM2 Workshop - Session D [Keynote] Adversarial Machine Learning Ananthram Swami (ARL, USA) [Invited] Vulnerabilities of Voice Assistants at the Edge: From Defeating Hidden Voice Attacks to Audio-based Adversarial Attacks Yingying (Jennifer) Chen (Rutgers University, USA) Adversarial Machine Learning Ananthram Swami (ARL, USA) Ananthram Swami is with the US Army CCDC Army Research Laboratory and is the Army's Senior Research Scientist (ST) for Network Science. Prior to joining ARL, he held positions with Unocal Corporation, the University of Southern California, CS-3 and Malgudi Systems. He was a Statistical Consultant to the California Lottery, developed a MATLAB-based toolbox for non-Gaussian signal processing. He has held visiting faculty positions at INP, Toulouse and Imperial College, London. He received the B.Tech. degree from IIT-Bombay; the M.S. degree from Rice University, and the Ph.D. degree from the University of Southern California (USC), all in Electrical Engineering. Swami's work is in the broad area of network science. He is an ARL Fellow and a Fellow of the IEEE. Adversarial Machine Learning. Modern machine learning systems are susceptible to adversarial examples; inputs that preserve the characteristic semantics of a given class, but whose classification is incorrect. Current approaches to defense against adversarial attacks rely on modifications to the input (e.g. quantization, randomization) or to the learned model parameters (e.g. via adversarial training), but are not always successful. This talk will include: 1) Overview of attacks on machine learning and defenses. 2) Discussion of the enablers of successful adversarial attacks via theory, and empirical analysis of commonly used datasets. 3) Discussion of recently proposed defenses that change the representation of the model outputs, drawing upon insights from coding theory. 4) Novel approaches to detection of adversarial examples using confidence metrics. The talk will conclude with a discussion of issues in distributed ML in coalition operations. Vulnerabilities of Voice Assistants at the Edge: From Defeating Hidden Voice Attacks to Audio-based Adversarial Attacks Yingying (Jennifer) Chen (Rutgers University, USA) Yingying (Jennifer) Chen is a Professor of Electrical and Computer Engineering and Peter Cherasia Faculty Scholar Endowed Professor at Rutgers University. She is the Associate Director of Wireless Information Network Laboratory (WINLAB). She also leads the Data Analysis and Information Security (DAISY) Lab. She is an IEEE Fellow. Her research