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Advances in photo editing and manipulation tools have made it significantly easier to create fake imagery.
Anomaly detection in crowded scenes
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Ng, T.T., Chang, S.F.: · 2004
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Digital image forensics via intrinsic fingerprints
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Priors for large photo collections and what they reveal about cameras
Kuthirummal, S., Agarwala, A., Goldman, D.B., Nayar, S.K.: · 2008
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Patchmatch: A randomized correspondence algorithm for structural image editing
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Using noise inconsistencies for blind image forensics
Mahdian, B., Saic, S.: · 2009
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ImageNet: A Large-Scale Hierarchical Image Database
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Jpeg error analysis and its applications to digital image forensics
Luo, W., Huang, J., Qiu, G.: · 2010
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Detecting double jpeg compression with the same quantization matrix
Huang, F., Huang, J., Shi, Y.Q.: · 2010
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Detection of misaligned cropping and recompression with the same quantization matrix and relevant forgery
Liu, Q.: · 2011
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Exposing digital image forgeries by illumination color classification
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Intriguing properties of neural networks
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Max-margin early event detectors
Hoai, M., De la Torre, F.: · 2014
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Kingma, D.P., Ba, J.: · 2014
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Generative adversarial networks
Ian J. Goodfellow, Y.B.: · 2014
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Zhu, J.Y., Krahenbuhl, P., Shechtman, E., Efros, A.A.: · 2015
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Aligned and non-aligned double JPEG detection using convolutional neural networks
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Agrawal, P., Carreira, J., Malik, J.: · 2015
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Farid, H.: · 2016
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Context encoders: Feature learning by inpainting
Pathak, D., Krahenbuhl, P., Donahue, J., Darrell, T., Efros, A.A.: · 2016
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Ambient sound provides supervision for visual learning
Owens, A., Wu, J., McDermott, J.H., Freeman, W.T., Torralba, A.: · 2016
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Learning visual groups from co-occurrences in space and time
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Barni, M., Bondi, L., Bonettini, N., Bestagini, P., Costanzo, A., Maggini, M., Tondi, B., Tubaro, S.: · 2017
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Localization of jpeg double compression through multi-domain convolutional neural networks
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Contrast enhancement estimation for digital image forensics
Wen, L., Qi, H., Lyu, S.: · 2017
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First steps toward camera model identification with convolutional neural networks
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Tampering detection and localization through clustering of camera-based cnn features
Bondi, L., Lameri, S., Güera, D., Bestagini, P., Delp, E.J., Tubaro, S.: · 2017
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Two-stream neural networks for tampered face detection
Zhou, P., Han, X., Morariu, V.I., Davis, L.S.: · 2017
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Detection of metadata tampering through discrepancy between image content and metadata using multi-task deep learning
Chen, B.C., Ghosh, P., Morariu, V.I., Davis., L.S.: · 2017
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Split-brain autoencoders: Unsupervised learning by cross-channel prediction
Zhang, R., Isola, P., Efros, A.A.: · 2017
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Detecting digital image forgeries by measuring inconsistencies of blocking artifact
Ye, S., Sun, Q., Chang, E.C.: · 2017
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Learning rich features for image manipulation detection
Zhou, P., Han, X., Morariu, V.I., Davis, L.S.: · 2018
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Learned forensic source similarity for unknown camra models
Owen Mayer, M.C.S.: · 2018
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Image provenance analysis at scale
Moreira, D., Bharati, A., Brogan, J., Pinto, A., Parowski, M., Bowyer, K.W., Flynn, P.J., Rocha, A., Scheirer, W.J.: · 2018
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