Using genetic algorithm to "fool" HMAX object recognition model

Abstract

HMAX ("Hierarchical Model and X") system is among the best machine vision approaches developed today, in many object recognition tasks [1]. HMAX decomposes an image into features which are passed to a classifier. These features each capture information about a small section of the input image but might not have information about the overall structure of the image if there is not a significant number of overlapping features. Therefore it can produce a false-positive if two images from two different classes having sufficiently similar features profile but completely different structures. To demonstrate the problem this thesis aimed to show that the features of a given subject are not unique because they lack geometric information. Genetic algorithm (GA) was used to create an image with a similar feature profile as a subject but which clearly does not belong to the subject. Using GA, random pixel images converged to an image whose feature profile has a small Euclidian distance from a target profile. This generated GA image does not resemble the target image but has a similar profile which successfully fooled the classifier in most cases. This implies that the "binding problem" is a major issue in a HMAX model of the size tested. Furthermore, methods of improving the system were investigated.