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Due to limited computational and memory resources, current deep learning models accept only rather small images in input, calling for preliminary image resizing.
G. Schaefer and M. Stich, “Ucid: an uncompressed color image database,” in Storage and Retrieval Methods and Applications for Multimedia 2004 , vol. 5307, 2003, pp. 472–480
2003
Earlier work this paper cites.
S. Lyu and H. Farid, “How realistic is photorealistic?” IEEE Trans. Signal Process. , vol. 53, no. 2, pp. 845–850, 2005
2005
Earlier work this paper cites.
S. Ye, Q. Sun, and E.-C. Chang, “Detecting digital image forgeries by measuring inconsistencies of blocking artifact,” in IEEE International Conference on Multimedia and Expo , 2007, pp. 12–15
2007
Earlier work this paper cites.
M. Chen, J. Fridrich, M. Goljan, and J. Lukàš, “Determining image origin and integrity using sensor noise,” IEEE Trans. Inf. Forensics Security , vol. 3, no. 4, pp. 74–90, 2008
2008
Earlier work this paper cites.
Y. Shi, C. Chen, and G. Xuan, “Steganalysis versus splicing detection,” in International Workshop on Digital Watermarking , vol. 5041, 2008, pp. 158–172
2008
Earlier work this paper cites.
B. Mahdian and S. Saic, “Using noise inconsistencies for blind image forensics,” Image and Vision Computing , vol. 27, no. 10, pp. 1497–1503, 2009
2009
Earlier work this paper cites.
M. Kirchner and J. Fridrich, “On detection of median filtering in digital images,” in SPIE, Electronic Imaging, Media Forensics and Security XII , vol. 7541, 2010, pp. 101–112
2010
Earlier work this paper cites.
T. Gloe and R. Böhme, “The ‘Dresden Image Database’ for benchmarking digital image forensics,” in Proceedings of the 25th Annual ACM Symposium On Applied Computing (SAC 2010) , vol. 2, Sierre, Switzerland, Mar. 2010, pp. 1585–1591
2010
Earlier work this paper cites.
P. Ferrara, T. Bianchi, A. D. Rosa, and A. Piva, “Image forgery localization via fine-grained analysis of CFA artifacts,” IEEE Trans. Inf. Forensics Security , vol. 7, no. 5, pp. 1566–1577, 2012
2012
Earlier work this paper cites.
T. Bianchi and A. Piva, “Image Forgery Localization via Block-Grained Analysis of JPEG Artifacts,” IEEE Trans. Inf. Forensics Security , vol. 7, no. 3, pp. 1003–1017, 2012
2012
Earlier work this paper cites.
Z. He, W. Lu, W. Sun, and J. Huang, “Digital image splicing detection based on Markov features in DCT and DWT domain,” Pattern recognition , vol. 45, pp. 4292–4299, 2012
2012
Earlier work this paper cites.
J. Fridrich and J. Kodovsky, “Rich models for steganalysis of digital images,” IEEE Trans. Inf. Forensics Security , vol. 7, pp. 868–882, 2012
2012
Earlier work this paper cites.
V. Christlein, C. Riess, J. Jordan, C. Riess, and E. Angelopoulou, “An evaluation of popular copy-move forgery detection approaches,” IEEE Transactions on information forensics and security , vol. 7, no. 6, pp. 1841–1854, 2012
2012
Earlier work this paper cites.
T. de Carvalho, C. Riess, E. Angelopoulou, H. Pedrini, and A. Rocha, “Exposing digital image forgeries by illumination color classification,” IEEE Trans. Inf. Forensics Security , vol. 8, no. 7, pp. 1182–1194, 2013
2013
Earlier work this paper cites.
G. Chierchia, G. Poggi, C. Sansone, and L. Verdoliva, “A Bayesian-MRF approach for PRNU-based image forgery detection,” IEEE Trans. Inf. Forensics Security , vol. 9, no. 4, pp. 554–567, 2014
2014
Earlier work this paper cites.
D. Cozzolino, D. Gragnaniello, and L. Verdoliva, “Image forgery detection through residual-based local descriptors and block-matching,” in IEEE International Conference on Image Processing , 2014, pp. 5297–5301
2014
Cited alongside, same era.
X. Zhao, S. Wang, S. Li, and J. Li, “Passive Image-Splicing Detection by a 2-D Noncausal Markov Model,” IEEE Trans. Circuits Syst. Video Technol. , vol. 25, no. 2, pp. 185–199, 2015
2015
Cited alongside, same era.
D. Cozzolino, G. Poggi, and L. Verdoliva, “Splicebuster: A new blind image splicing detector,” in IEEE International Workshop on Information Forensics and Security , 2015, pp. 1–6
2015
Cited alongside, same era.
Y. Rao and J. Ni, “A deep learning approach to detection of splicing and copy-move forgeries in images,” in IEEE International Workshop on Information Forensics and Security , 2016, pp. 1–6
2016
Cited alongside, same era.
R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra, “Grad-cam: Visual explanations from deep networks via gradient-based localization,” in Proceedings of the IEEE International Conference on Computer Vision , 2017, pp. 618–626
2017
Later among the works it cites.
H. Li, W. Luo, X. Qiu, and J. Huang, “Identification of various image operations using residual-based features,” IEEE Trans. Circuits Syst. Video Technol. , vol. 28, no. 1, pp. 31–45, 2018
2018
Later among the works it cites.
Y. Liu, Q. Guan, X. Zhao, and Y. Cao, “Image forgery localization based on multi-scale convolutional neural networks,” in ACM Workshop on Information Hiding and Multimedia Security , 2018
2018
Later among the works it cites.
P. Zhou, X. Han, V. Morariu, and L. Davis, “Learning rich features for image manipulation detection,” in IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 1053–1061
2018
Later among the works it cites.
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B. Bayar and M. Stamm, “A deep learning approach to universal image manipulation detection using a new convolutional layer,” in ACM Workshop on Information Hiding and Multimedia Security , 2016
2016
Cited alongside, same era.
D. Cozzolino and L. Verdoliva, “Single-image splicing localization through autoencoder-based anomaly detection,” in IEEE Workshop on Information Forensics and Security , 2016, pp. 1–6
2016
Cited alongside, same era.
2016
Cited alongside, same era.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 770–778
2016
Cited alongside, same era.
D. Cozzolino, G. Poggi, and L. Verdoliva, “Recasting residual-based local descriptors as convolutional neural networks: an application to image forgery detection,” in ACM Workshop on Information Hiding and Multimedia Security , 2017, pp. 1–6
2017
Cited alongside, same era.
L. Bondi, S. Lameri, D. Güera, P. Bestagini, E. Delp, and S. Tubaro, “Tampering Detection and Localization through Clustering of Camera-Based CNN Features,” in IEEE CVPR Workshops , 2017
2017
Cited alongside, same era.
D. Shullani, M. Fontani, M. Iuliani, O. A. Shaya, and A. Piva, “Vision: a video and image dataset for source identification,” EURASIP Journal on Information Security , pp. 1–16, 2017
2017
Cited alongside, same era.
F. Chollet, “Xception: Deep Learning with Depthwise Separable Convolutions,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2017
2017
Cited alongside, same era.
F. Marra, D. Gragnaniello, D. Cozzolino, and L. Verdoliva, “Detection of GAN-generated fake images over social networks,” in 1st IEEE International Workshop on “Fake MultiMedia” , April 2018
2018
Later among the works it cites.
R. Salloum, Y. Ren, and C. C. J. Kuo, “Image Splicing Localization using a Multi-task Fully Convolutional Network (MFCN),” Journal of Visual Communication and Image Representation , vol. 51, pp. 201–209, 2018
2018
Later among the works it cites.
Z. Zhang, Y. Zhang, Z. Zhou, and J. Luo, “Boundary-based Image Forgery Detection by Fast Shallow CNN,” in IEEE International Conference on Pattern Recognition , 2018, pp. 2658–2663
2018
Later among the works it cites.
2018
Later among the works it cites.
——, “Camera-based image forgery localization using convolutional neural networks,” in European Signal Processing Conference , Sep. 2018
2018
Later among the works it cites.
M. Huh, A. Liu, A. Owens, and A. Efros, “Fighting fake news: Image splice detection via learned self-consistency,” in European Conference on Computer Vision , 2018, pp. 101–117
2018
Later among the works it cites.
A. Roessler, D. Cozzolino, L. Verdoliva, C. Riess, J. Thies, and M. Nießner, “FaceForensics++: Learning to Detect Manipulated Facial Images,” in International Conference on Computer Vision , 2019
2019
Closest in time.
J. Bappy, C. Simons, L. Nataraj, B. Manjunath, and A. Roy-Chowdhury, “Hybrid LSTM and Encoder-Decoder Architecture for Detection of Image Forgeries,” IEEE Transactions on Image Processing , vol. 28, no. 7, pp. 3286–3300, 2019
2019
Closest in time.
D. Cozzolino and L. Verdoliva, “Noiseprint: a CNN-based camera model fingerprint,” IEEE Trans. Inf. Forensics Security, in press , 2019
2019
Closest in time.
H. Guan, M. Kozak, E. Robertson, Y. Lee, A. N. Yates, A. Delgado, D. Zhou, T. Kheyrkhah, J. Smith, and J. Fiscus, “Mfc datasets: Large-scale benchmark datasets for media forensic challenge evaluation,” in 2019 IEEE Winter Applications of Computer Vision Workshops (WACVW) . IEEE, 2019, pp. 63–72
2019
Closest in time.