WU Xianyan, HAN Qi, NIU Xiamu, ZHANG Hongli. Novel Similarity Measurements for Reassembling Fragmented Image Files[J]. Chinese Journal of Electronics, 2019, 28(2): 331-337. doi: 10.1049/cje.2019.01.016
Citation: WU Xianyan, HAN Qi, NIU Xiamu, ZHANG Hongli. Novel Similarity Measurements for Reassembling Fragmented Image Files[J]. Chinese Journal of Electronics, 2019, 28(2): 331-337. doi: 10.1049/cje.2019.01.016

Novel Similarity Measurements for Reassembling Fragmented Image Files

doi: 10.1049/cje.2019.01.016
Funds:  This work is supported by the National Natural Science Foundation of China (No.61471141, No.61301099, No.61361166006), HK(No.N_HKU 729/13), and the Fundamental Research Funds for the Central Universities (No.HIT. KISTP. 201416, No.HIT. KISTP. 201414).
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  • Corresponding author: HAN Qi (corresponding author) received the B.S. degree and M.S. degree from Harbin Institute of Technology, Harbin, P.R. China in 2002 and 2004 respectively, and received the Ph.D. degree in Computer Science and Technology from Harbin Institute of Technology, 2009. He is ACM member. His current research fields include digital video forensic, hiding communication and digital watermarking. (Email:qi.han@hit.edu.cn)
  • Received Date: 2016-06-10
  • Rev Recd Date: 2017-02-22
  • Publish Date: 2019-03-10
  • Reassembling fragmented image files is a useful technique to seize image evidence in digital forensics. A key problem of reassembly is how to measure the similarity between the fragments. Most of the measurements are based on the local similarity of the images. We analyze the impact of similarity patterns on the judgment of the adjacency of the fragments and conclude that the horizontal similarity has little help. According to this conclusion, we improve the median edge detector by replacing the horizontal similarity with the left and right diagonal similarity. Furthermore, we improve the sum of differences and Euclidean distance by replacing the mean/sum used in the two measurements with the median. Experimental results verify the analysis of similarity patterns and the improvements.
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