1 |
Z. Zhang, Q. Liu, and Y. Wang, "Road extraction by deep residual u-net," IEEE Geoscience and Remote Sensing Letters 15, no. 5 (2018): 749-753.
DOI
|
2 |
M. Holschneider, R. Kronland-Martinet, J. Morlet, and P.H. Tchamitchian, "A real-time algorithm for signal analysis with the help of the wavelet transform," In Wavelets: Time-Frequency Methods and Phase Space. Proceedings of the International Conference, 1987.
|
3 |
F. Sundus, M. H. Yousaf, and F. Hussain. "A generic passive image forgery detection scheme using local binary pattern with rich models." Computers & Electrical Engineering 62 (2017): 459-472.
DOI
|
4 |
R. Salloum, Y. Ren, and C. Kuo, "Image splicing localization using a multi-task fully convolutional network," (mfcn). arXiv 2017.
|
5 |
I. Amerini, L. Ballan, R. Caldelli, A. Del Bimbo, G. Serra. "A SIFT-based forensic method for copy-move attack detection and transformation recovery", IEEE Transactions on Information Forensics and Security, vol. 6, issue 3, pp. 1099-1110, 2011.
DOI
|
6 |
P. Ferrara, T. Bianchi, A. De Rosa, and A. Piva. Image forgery localization via fine-grained analysis of CFA artifacts. TIFS, 2012.
|
7 |
N. Krawetz. A picture's worth... Hacker Factor Solutions, 2007.
|
8 |
J. Dong, W. Wang, and T. Tan. Casia image tampering detection evaluation database. In ChinaSIP, 2013.
|
9 |
Nist nimble 2016 datasets. https://www.nist.gov/itl/iad/mig/ nimble-challenge-2017-evaluation/.
|
10 |
J. Dong, W. Wang, and T. Tan. Casia image tampering detection evaluation database 2010. http://forensics.idealtest.org.
|
11 |
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, "Going deeper with convolutions," in CVPR, 2015, pp. 1-9.
|
12 |
T. Carvalho, F. A. Faria, H. Pedrini, R. Da S. Torres, and A. Rocha, "Illuminant-based transformed spaces for image forensics," IEEE Trans. Inf. Forensics Security, vol. 11, no. 4, pp. 720-733, Apr. 2016
DOI
|
13 |
A. Piva, "An overview on image forensics," ISRN Signal Processing, (2013).
|
14 |
P. Kakar, "Passive Approaches for Detecting Digital Image Forgery", Ph.D. Thesis, Nanyang Technological University, 2012.
|
15 |
P. Zhou, X. Han, V. I. Morariu, and L. S. Davis, "Learning rich features for image manipulation detection," In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1053-1061. 2018.
|
16 |
X. Qiu, H. Li, W. Luo, and J. Huang "A universal image forensics strategy based on steganalytic model," In Proceedings of the 2nd ACM workshop on Information hiding and multimedia security, (pp. 165-170), ACM (2014 Jun 11).
|
17 |
H. Farid, "A survey of image forgery detection," IEEE Signal Process Mag 2:16-25, (2009).
DOI
|
18 |
K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," in CVPR, 2016, pp. 770-778.
|
19 |
O. Ronneberger, F. Philipp, and B. Thomas, "U-net: Convolutional networks for biomedical image segmentation," In International Conference on Medical image computing and computer-assisted intervention, pp. 234-241. Springer, Cham, 2015.
|
20 |
F. Yu, and K. Vladlen, "Multi-scale context aggregation by dilated convolutions," arXiv preprint arXiv: 1511.07122 (2015).
|
21 |
K. Simonyan and A. Zisserman, "Very deep convolutional networks for large-scale image recognition," arXiv: 1409.1556, 2014.
|
22 |
J. H. Bappy, C. Simons, L. Nataraj, B. S. Manjunath, and A. K. RoyChowdhury, "Hybrid LSTM and encoder-decoder architecture for detection of image forgeries," IEEE Trans. Image Process., vol. 28, no. 7, pp. 3286-3300, Jul. 2019.
DOI
|
23 |
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 (WIFS) (pp. 1-6), IEEE, 2016.
|
24 |
W. Yue, W. AbdAlmageed, and P. Natarajan, "ManTra-Net: Manipulation tracing network for detection and localization of image forgeries with anomalous features," In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9543-9552. 2019.
|
25 |
T. Pomari, G. Ruppert, E. Rezende, A. Rocha, and T. Carvalho, "Image splicing detection through illumination inconsistencies and deep learning," In 2018 25th IEEE International Conference on Image Processing (ICIP) (pp. 3788-3792). IEEE, 2018.
|
26 |
M. Goljan, F. Jessica, and C. Remi, "Rich model for steganalysis of color images," In 2014 IEEE International Workshop on Information Forensics and Security (WIFS), pp. 185-190. IEEE, 2014.
|
27 |
L. Baby, and A. Jose, "Detection of Splicing in Digital Images Based on Illuminant Features," International Journal of Innovation and Scientific Research, 11(2351-8014), p.4, 2014
|
28 |
Y. Abdalla, M.T. Iqbal, and M.Shehata, "Copy-Move Forgery Detection and Localization Using a Generative Adversarial Network and Convolutional Neural-Network," Information, 10(9), p.286, (2019).
DOI
|
29 |
L. Li L, J. Xue, X. Wang, and L. Tian, "A robust approach to detect digital forgeries by exploring correlation patterns," Pattern Anal Appl 1-15, 2013.
|
30 |
M. Boroumand, and J. Fridrich, "Deep learning for detecting processing history of images," Society for Imaging Science and Technology, (2018).
|
31 |
D. Cozzolino and L. Verdoliva. Single-image splicing localization through autoencoder-based anomaly detection. In WIFS, 2016.
|
32 |
Y.F. Hsu, and S. Chang "Detecting Image Splicing Using Geometry Invariants And Camera Characteristics Consistency," in International Conference on Multimedia and Expo (ICME), Toronto, Canada, July 2006
|
33 |
S. Vesal, N. Ravikumar, and A. Maier, "A 2D dilated residual U-Net for multi-organ segmentation in thoracic CT." arXiv preprint arXiv: 1905.07710 (2019).
|
34 |
Y. Chen, K. Xiangui, Q. S. Yun, and Z. Jane Wang, "A multi-purpose image forensic method using densely connected convolutional neural networks," Journal of Real-Time Image Processing 16, no. 3 pp. 725-740, (2019).
DOI
|
35 |
Y. Zhan, Y. Chen, Q. Zhang, and X. Kang, "Image forensics based on transfer learning and convolutional neural network," In Proceedings of the 5th ACM Workshop on Information Hiding and Multimedia Security (pp. 165-170), 2017.
|
36 |
Y. Rao, J. Ni, and H. Zhao, "Deep Learning Local Descriptor for Image Splicing Detection and Localization," IEEE Access 8 (2020): 25611-25625.
DOI
|
37 |
R. Zhang, and J. Ni. "A Dense U-Net with Cross-Layer Intersection for Detection and Localization of Image Forgery." In ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2982-2986. IEEE, 2020.
|
38 |
K. He, X. Zhang, S. Ren, and J. Sun, "Identity mappings in deep residual networks," In European conference on computer vision, pp. 630-645. Springer, Cham, 2016.
|