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Efficient cropping-resistant robust image hashing

: Steinebach, Martin; Liu, Huajian; Yannikos, York


Institute of Electrical and Electronics Engineers -IEEE-; IEEE Computer Society:
Ninth International Conference on Availability, Reliability and Security, ARES 2014 : Fribourg, Switzerland, 8 - 12 September 2014; Including workshops
Los Alamitos, Calif.: IEEE Computer Society Conference Publishing Services (CPS), 2014
ISBN: 978-1-4799-4223-7
ISBN: 978-1-4799-7876-2
International Conference on Availability, Reliability, and Security (ARES) <9, 2014, Fribourg>
International Workshop on Digital Forensics (WSDF) <7, 2014, Fribourg>
Fraunhofer SIT ()

A digital forensics examiner often has to deal with large amounts of multimedia content during an investigation. One important part of such an investigation is to identify illegal material like pictures containing child pornography. Robust image hashing is an effective technique to help identifying known illegal images even after the original images were modified by applying various image processing operations. However, some specific operations lead to increased false negative rates when using robust image hashing. One of the most challenging operations today is image cropping. In this work we introduce an approach to counter cropping operations on images by combining image segmentation and efficient block mean image hashing. We show that we are able to achieve high detection rates for images where cropping operations where applied on the original known source. This further improves the robustness of our image hashing approach.