Using genetic algorithm to "fool" HMAX object recognition model

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.

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2012

Author dc:creator

• Hasan, Maysun Mazhar

Advisor dc:contributor.advisor

• Tomaso Poggio and Victor Chan.

• Electrical Engineering and Computer Science.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/77013

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/77013