Design of a Digital Forensics Image Mining System
Brown, Ross A., Pham, Binh L., & de Vel, Olivier Y. (2005) Design of a Digital Forensics Image Mining System. In The Workshop on Intelligent Information Hiding and Multimedia Signal Processing (IIHMSP05), September, Melbourne.
Increasing amount of illicit image data transmitted via the internet has triggered the need to develop effective image mining systems for digital forensics purposes. This paper discusses the requirements of digital image forensics which underpin the design of our forensic image mining system. This system can be trained by a hierarchical Support Vector Machine (SVM) to detect objects and scenes which are made up of components under spatial or non-spatial constraints. Forensic investigators can communicate with the system via a grammar which allows object description for training, searching, querying and relevance feedback. In addition, we propose to use a Bayesian networks approach to deal with information uncertainties which are inherent in forensic work. These inference networks will be constructed to model probability interactions between beliefs, adapt to different users’ retrieval patterns, and mimic human judgement of semantic content of image patches. An analysis of the performance of the first prototype of the system is also provided.
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|Item Type:||Conference Paper|
|Keywords:||Image Mining, Image Retrieval, Pornography Detection, Computer Forensics|
|Subjects:||Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > LIBRARY AND INFORMATION STUDIES (080700) > Information Retrieval and Web Search (080704)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Computer Vision (080104)
|Divisions:||Past > QUT Faculties & Divisions > Faculty of Science and Technology|
|Copyright Owner:||Copyright 2005 (please consult author)|
|Deposited On:||21 Oct 2005|
|Last Modified:||17 Apr 2015 02:19|
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