Publication: The effect of attacks on DCT-based features for image copy detection
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2015-01-01
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2-s2.0-84925237441
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Mahidol University
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SCOPUS
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Proceedings - 17th IEEE International Conference on Computational Science and Engineering, CSE 2014, Jointly with 13th IEEE International Conference on Ubiquitous Computing and Communications, IUCC 2014, 13th International Symposium on Pervasive Systems, Algorithms, and Networks, I-SPAN 2014 and 8th International Conference on Frontier of Computer Science and Technology, FCST 2014. (2015), 1483-1488
Suggested Citation
Nirin Thanirat, Sudsanguan Ngamsuriyaroj The effect of attacks on DCT-based features for image copy detection. Proceedings - 17th IEEE International Conference on Computational Science and Engineering, CSE 2014, Jointly with 13th IEEE International Conference on Ubiquitous Computing and Communications, IUCC 2014, 13th International Symposium on Pervasive Systems, Algorithms, and Networks, I-SPAN 2014 and 8th International Conference on Frontier of Computer Science and Technology, FCST 2014. (2015), 1483-1488. doi:10.1109/CSE.2014.276 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/35825
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Title
The effect of attacks on DCT-based features for image copy detection
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Abstract
© 2014 IEEE. Nowadays images have been easily reproduced and distributed globally very fast. For the protection, the detection is an important issue. Any modification on image copying processes can be viewed as attacks. The attacks include adding some small information as well as modifying images. In addition, the content-based copy detection techniques are relied on the image properties which could be extracted as features to be used in comparison between the original image and its copy. In this paper, we propose the DCT-based feature extraction technique which uses pixel values, edge, texture and frequency distribution of the image as features. A set of experiments have been conducted to evaluate several attacks in terms of robustness and sensitivity. The results show that when attacked, the image features react differently according to the types of attack. Different types of DCT-based features keep different piece of information and their robustness and sensitivity can show how features and attacks relate.