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In this work, we propose a technique that utilizes a fully convolutional network (FCN) to localize image splicing attacks.
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Bianchi, T., De Rosa, A., Piva, A., 2011. Improved dct coefficient analysis for forgery localization in jpeg images. In: Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference on. IEEE, pp. 2444–2447
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Ferrara, P., Bianchi, T., De Rosa, A., Piva, A., 2012. Image forgery localization via fine-grained analysis of cfa artifacts. IEEE Transactions on Information Forensics and Security 7 (5), 1566–1577
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Krizhevsky, A., Sutskever, I., Hinton, G. E., 2012. Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems. pp. 1097–1105
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Eigen, D., Fergus, R., 2015. Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 2650–2658
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De Carvalho, T. J., Riess, C., Angelopoulou, E., Pedrini, H., de Rezende Rocha, A., 2013. Exposing digital image forgeries by illumination color classification. IEEE Transactions on Information Forensics and Security 8 (7), 1182–1194
2013
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Amerini, I., Becarelli, R., Caldelli, R., Del Mastio, A., 2014. Splicing forgeries localization through the use of first digit features. In: Information Forensics and Security (WIFS), 2014 IEEE International Workshop on. IEEE, pp. 143–148
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Chierchia, G., Poggi, G., Sansone, C., Verdoliva, L., 2014. A bayesian-mrf approach for prnu-based image forgery detection. IEEE Transactions on Information Forensics and Security 9 (4), 554–567
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Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T., 2014. Caffe: Convolutional architecture for fast feature embedding. In: Proceedings of the 22nd ACM international conference on Multimedia. ACM, pp. 675–678
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Lyu, S., Pan, X., Zhang, X., 2014. Exposing region splicing forgeries with blind local noise estimation. International Journal of Computer Vision 110 (2), 202–221
2014
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2014
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2015
Cited alongside, same era.
Cozzolino, D., Poggi, G., Verdoliva, L., 2015. Splicebuster: a new blind image splicing detector. In: Information Forensics and Security (WIFS), 2015 IEEE International Workshop on. IEEE, pp. 1–6
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Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al., 2015. Imagenet large scale visual recognition challenge. International Journal of Computer Vision 115 (3), 211–252
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Cozzolino, D., Verdoliva, L., 2016. Single-image splicing localization through autoencoder-based anomaly detection. In: Information Forensics and Security (WIFS), 2016 IEEE International Workshop on. IEEE, pp. 1–6
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Rao, Y., Ni, J., 2016. A deep learning approach to detection of splicing and copy-move forgeries in images. In: Information Forensics and Security (WIFS), 2016 IEEE International Workshop on. IEEE, pp. 1–6
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Ren, Y., Li, S., Chen, C., Kuo, C.-C. J., 2016. A coarse-to-fine indoor layout estimation (cfile) method. In: Asian Conference on Computer Vision. Springer, pp. 36–51
2016
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Zampoglou, M., Papadopoulos, S., Kompatsiaris, Y., 2016. Large-scale evaluation of splicing localization algorithms for web images. Multimedia Tools and Applications, 1–34
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2016
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