Abstract
In this paper, we present a model that automatically differentiates copied versions of original images. Unlike traditional image copy detection schemes, our system is a Convolutional Neural Networks (CNN) based model which means that it does not need any manually-designed features. In addition, a convolutional network is more applicable to image copy detection whose architecture is designed for robustness to geometric distortions. Our model uses fully connected layers to compute a similarity between CNN features, which are extracted from image pairs by a deep convolutional network. This method is very efficient and scalable to large databases. In order to see the comparison visually, a variety of models are explored. Experimental results demonstrate that our model presents surprising performance on various data sets.
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References
Amerini, I., Ballan, L., Caldelli, R., Del Bimbo, A., Serra, G.: A sift-based forensic method for copycmove attack detection and transformation recovery. IEEE Trans. Inf. Forensics Secur. 6(3), 1099–1110 (2011)
Berrani, S.A., Amsaleg, L., Gros, P.: Robust content-based image searches for copyright protection. In: ACM International Workshop on Multimedia Databases, Acm-Mmdb 2003, New Orleans, Louisiana, USA, November, pp. 70–77 (2003)
Cao, Y., Zhang, H., Gao, Y., Guo, J.: An efficient duplicate image detection method based on affine-sift feature. In: 2010 3rd IEEE International Conference on Broadband Network and Multimedia Technology (IC-BNMT), pp. 794–797, October 2010
Christlein, V., Riess, C., Jordan, J., Riess, C., Angelopoulou, E.: An evaluation of popular copy-move forgery detection approaches. IEEE Trans. Inf. Forensics Secur. 7(6), 1841–1854 (2012)
Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587, June 2014
Hadsell, R., Chopra, S., LeCun, Y.: Dimensionality reduction by learning an invariant mapping. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2006), vol. 2, pp. 1735–1742 (2006)
Han, X., Leung, T., Jia, Y., Sukthankar, R., Berg, A.C.: MatchNet: unifying feature and metric learning for patch-based matching. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3279–3286, June 2015
Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.R.: Improving neural networks by preventing co-adaptation of feature detectors. Comput. Sci. 3(4), 212–223 (2012)
Indyk, P., Motwani, R.: Approximate nearest neighbors: towards removing the curse of dimensionality. In: Proceedings of the Thirtieth Annual ACM Symposium on Theory of Computing, STOC 1998, NY, USA (1998). http://doi.acm.org/10.1145/276698.276876
Jégou, H., Douze, M., Schmid, C.: Hamming embedding and weak geometric consistency for large scale image search. In: Proceedings of the 10th European Conference on Computer Vision, p. 1.1, October 2008
Jégou, H., Douze, M., Schmid, C., Pérez, P.: Aggregating local descriptors into a compact image representation. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3304–3311, June 2010
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: Convolutional architecture for fast feature embedding. Eprint Arxiv, pp. 675–678 (2014)
Krapac, J., Allan, M., Verbeek, J., Juried, F.: Improving web image search results using query-relative classifiers. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1094–1101, June 2010
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst. 25(2), 1097–1105 (2012)
Li, P., Wang, M., Cheng, J., Xu, C., Lu, H.: Spectral hashing with semantically consistent graph for image indexing. IEEE Trans. Multimedia 15(1), 141–152 (2013)
Li, Z., Liu, G., Jiang, H., Qian, X.: Image copy detection using a robust gabor texture descriptor. In: Proceedings of the First ACM Workshop on Large-Scale Multimedia Retrieval and Mining, LS-MMRM 2009, NY, USA, pp. 65–72 (2009). http://doi.acm.org/10.1145/1631058.1631072
Ling, H., Yan, L., Zou, F., Liu, C., Feng, H.: Fast image copy detection approach based on local fingerprint defined visual words. Signal Process. 93(8), 2328–2338 (2013)
Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Trans. Pattern Anal. Mach. Intell. 27(10), 1615–1630 (2005)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. Eprint Arxiv (2014)
Sivic, J., Zisserman, A.: Video google: a text retrieval approach to object matching in videos. In: Ninth IEEE International Conference on Computer Vision, 2003. Proceedings, vol. 2, pp. 1470–1477, October 2003
Tang, Z., Huang, L., Yang, F., Zhang, X.: Robust image hashing based on fan-beam transform. ICIC Express Lett. 8(8), 2365–2372 (2014)
Tang, Z., Yang, F., Huang, L., Wei, M.: DCT and DWT based image hashing for copy detection. ICIC Express Lett. 7(11), 2961–2967 (2013)
Tirilly, P., Claveau, V., Gros, P.: Language modeling for bag-of-visual words image categorization. In: Proceedings of the 2008 International Conference on Content-based Image and Video Retrieval, CIVR 2008, NY, USA, pp. 249–258 (2008). http://doi.acm.org/10.1145/1386352.1386388
Tralic, D., Zupancic, I., Grgic, S., Grgic, M.: CoMoFoD - new database for copy-move forgery detection. In: 55th International Symposium ELMAR-2013, pp. 49–54, September 2013
Žbontar, J., LeCun, Y.: Computing the stereo matching cost with a convolutional neural network. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1592–1599, June 2015
Zagoruyko, S., Komodakis, N.: Learning to compare image patches via convolutional neural networks. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4353–4361, June 2015
Zhang, D., Wang, J., Cai, D., Lu, J.: Self-taught hashing for fast similarity search. In: Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2010, NY, USA, pp. 18–25 (2010). http://doi.acm.org/10.1145/1835449.1835455
Zheng, Q.F., Wang, W.Q., Gao, W.: Effective and efficient object-based image retrieval using visual phrases. In: Proceedings of the 14th ACM International Conference on Multimedia, MM 2006, NY, USA, pp. 77–80 (2006). http://doi.acm.org/10.1145/1180639.1180664
Acknowledgement
This work is partly supported by the 973 basic research program of China (Grant No. 2014CB349303), the Natural Science Foundation of China (Grant No. 61472421), the National Nature Science Foundation of China (No. 61370038) and the Strategic Priority Research Program of the CAS (Grant No. XDB02070003).
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Zhang, J., Zhu, W., Li, B., Hu, W., Yang, J. (2016). Image Copy Detection Based on Convolutional Neural Networks. In: Tan, T., Li, X., Chen, X., Zhou, J., Yang, J., Cheng, H. (eds) Pattern Recognition. CCPR 2016. Communications in Computer and Information Science, vol 663. Springer, Singapore. https://doi.org/10.1007/978-981-10-3005-5_10
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