Detecting a Corrupted Area in a 2-Dimensional Space

Abstract

Motivated by the fact that 2-dimensional data have become popularly used in many applications without being much considered its integrity checking. We introduce the problem of detecting a corrupted area in a 2-dimensional space, and investigate two possible efficient approaches and show their time and space complexities. Also, we briefly introduce the idea of an approximation scheme using a hash sieve and suggest a novel "adaptive tree" structure revealing granularity of information.