2020 IEEE International Symposium
on Information Theory
21-26 June 2020 • Los Angeles, California, USA
| Paper ID | E.5.1 | ||
| Paper Title | Evasive Active Hypothesis Testing | ||
| Authors | Meng-Che Chang, Matthieu Bloch, Georgia Institution of Technology, United States | ||
| Session | E.5: Hypothesis Testing I | ||
| Presentation | Lecture | ||
| Track | Detection and Estimation | ||
| Manuscript | Click here to download the manuscript | ||
| Virtual Presentation | Click here to watch in the Virtual Symposium | ||
| Abstract | We consider an active hypothesis testing scenario in which an adversary obtains observations while legitimate parties engage in a sequential adaptive control policy to estimate an unknown parameter. We formulate an evasive active hypothesis problem in which the objective is for the legitimate parties to control the risk of their test while minimizing the detection ability of the adversary, measured in terms of its error exponent. We develop bounds on the adversary's error exponent that offer insight into how legitimate adversaries can evade the adversary's detection. We illustrate the results in a wireless transmission detection example. | ||
Plan Ahead
2021 IEEE International Symposium on Information Theory
11-16 July 2021 | Melbourne, Victoria, Australia
