Very fast concentric circle partition-based replica detection method

Authors:
Ik-Hwan Cho;A-Young Cho;Jun-Woo Lee;Ju-Kyung Jin;Won-Keun Yang;Weon-Geun Oh;Dong-Seok Jeong
Affiliations:
Dept. of Electronic Engineering, Inha University, Incheon, Republic of Korea;Dept. of Electronic Engineering, Inha University, Incheon, Republic of Korea;Dept. of Electronic Engineering, Inha University, Incheon, Republic of Korea;Dept. of Electronic Engineering, Inha University, Incheon, Republic of Korea;Dept. of Electronic Engineering, Inha University, Incheon, Republic of Korea;Electronics and Telecommunication Research Institute, Daejeon, Republic of Korea;Dept. of Electronic Engineering, Inha University, Incheon, Republic of Korea
Venue:
PSIVT'07 Proceedings of the 2nd Pacific Rim conference on Advances in image and video technology
Year:
2007

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Abstract

Image replica detection becomes very active research field recently as the electronic device such as the digital camera which generates digital images spreads out rapidly. As huge amount of digital images leads to severe problems like copyright protection, the necessity of replica detection system gets more and more attention. In this paper, we propose a new fast image replica detector based on concentric circle partition method. The proposed algorithm partitions image into concentric circle with fixed angle from image center position outwards. From these partitioned regions, total of four features are extracted. They are average intensity distribution and its difference, symmetrical difference distribution and circular difference distribution in bitstring type. To evaluate the performance of the proposed method, pairwise independence test and accuracy test are applied. We compare the duplicate detection performance of the proposed algorithm with that of the MPEG-7 visual descriptors. From experimental results, we can tell that the proposed method shows very high matching speed and high accuracy on the detection of replicas which go through many modification from the original. Because we use the hash code as the image signature, the matching process needs very short computation time. And the proposed method shows 97.6% accuracy on average under 1 part per million false positive rate.