Binary Hypothesis Testing Game with Training Data

TL;DR

This paper develops a game-theoretic framework for binary hypothesis testing with training data, analyzing the impact of adversarial attacks on decision accuracy and introducing the concept of indistinguishability regions.

Contribution

It introduces a novel game-theoretic model for hypothesis testing with training data, deriving asymptotic equilibrium and defining indistinguishability regions under attack scenarios.

Findings

Derived asymptotic equilibrium of the game.

Defined the indistinguishability region for pmfs under attack.

Compared results with scenarios where pmf is perfectly known.

Abstract

We introduce a game-theoretic framework to study the hypothesis testing problem, in the presence of an adversary aiming at preventing a correct decision. Specifically, the paper considers a scenario in which an analyst has to decide whether a test sequence has been drawn according to a probability mass function (pmf) P_X or not. In turn, the goal of the adversary is to take a sequence generated according to a different pmf and modify it in such a way to induce a decision error. P_X is known only through one or more training sequences. We derive the asymptotic equilibrium of the game under the assumption that the analyst relies only on first order statistics of the test sequence, and compute the asymptotic payoff of the game when the length of the test sequence tends to infinity. We introduce the concept of indistinguishability region, as the set of pmf's that can not be distinguished reliably from P_X in the presence of attacks. Two different scenarios are considered: in the first one the analyst and the adversary share the same training sequence, in the second scenario, they rely on independent sequences. The obtained results are compared to a version of the game in which the pmf P_X is perfectly known to the analyst and the adversary.