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Due to the wide diffusion of JPEG coding standard, the image forensic community has devoted significant attention to the development of double JPEG (DJPEG) compression detectors through the years.
An effective method for detecting double JPEG compression with the same quantization matrix,
J. Yang, J. Xie, G. Zhu, S. Kwong, Y.-Q. Shi, · 1942
Earlier work this paper cites.
Convolutional networks for images, speech, and time-series,
Y. LeCun, Y. Bengio, · 1995
Earlier work this paper cites.
Gradient-based learning applied to document recognition,
Y. LeCun, L. Bottou, Y. Bengio, P. Haffner, · 1998
Earlier work this paper cites.
Spatially adaptive statistical modeling of wavelet image coefficients and its application to denoising,
M. K. Mihcak, I. Kozintsev, K. Ramchandran, · 1999
Earlier work this paper cites.
Statistical tools for digital forensics,
A. C. Popescu, H. Farid, · 2004
Earlier work this paper cites.
Digital camera identification from sensor pattern noise,
J. Lukáš, J. Fridrich, M. Goljan, · 2006
Earlier work this paper cites.
A novel method for detecting cropped and recompressed image block,
W. Luo, Z. Qu, J. Huang, G. Qiu, · 2007
Earlier work this paper cites.
A machine learning based scheme for double JPEG compression detection,
C. Chen, Y. Q. Shi, W. Su, · 2008
Earlier work this paper cites.
Detection of double-compression in JPEG images for applications in steganography,
T. Pevny, J. Fridrich, · 2008
Earlier work this paper cites.
Detecting doubly compressed JPEG images by using mode based first digit features,
B. Li, Y. Shi, J. Huang, · 2008
Earlier work this paper cites.
A convolutive mixing model for shifted double JPEG compression with application to passive image authentication,
Z. Qu, W. Luo, J. Huang, · 2008
Earlier work this paper cites.
Fast, automatic and fine-grained tampered JPEG image detection via DCT coefficient analysis,
Z. Lin, J. He, X. Tang, C.-K. Tang, · 2009
Earlier work this paper cites.
Learning deep architectures for AI,
Y. Bengio, · 2009
Cited alongside, same era.
Rectified linear units improve restricted Boltzmann machines,
V. Nair, G. Hinton, · 2010
Cited alongside, same era.
Vision of the unseen: Current trends and challenges in digital image and video forensics,
A. Rocha, W. Scheirer, T. Boult, S. Goldenstein, · 2011
Cited alongside, same era.
Improved DCT coefficient analysis for forgery localization in JPEG images,
T. Bianchi, A. De Rosa, A. Piva, · 2011
Cited alongside, same era.
Detecting recompression of JPEG images via periodicity analysis of compression artifacts for tampering detection,
Y.-L. Chen, C.-T. Hsu, · 2011
Cited alongside, same era.
An overview on image forensics,
A. Piva, · 2013
Cited alongside, same era.
Quantization-unaware double jpeg compression detection,
A. Taimori, F. Razzazi, A. Behrad, A. Ahmadi, M. Babaie-Zadeh, · 2016
Later among the works it cites.
Forensics of high quality and nearly identical jpeg image recompression,
C. Pasquini, P. Schöttle, R. Böhme, G. Boato, F. Pèrez-Gonzàlez, · 2016
Later among the works it cites.
Double JPEG compression forensics based on a convolutional neural network,
Q. Wang, R. Zhang, · 2016
Later among the works it cites.
Deep learning for steganalysis is better than a rich model with an ensemble classifier, and is natively robust to the cover source-mismatch,
L. Pibre, P. Jérôme, D. Ienco, M. Chaumont, · 2016
Later among the works it cites.
Structural design of convolutional neural networks for steganalysis,
G. Xu, H. Z. Wu, Y. Q. Shi, · 2016
Later among the works it cites.
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Multiple jpeg compression detection by means of benford-fourier coefficients,
C. Pasquini, G. Boato, F. Perez-Gonzalez, · 2014
Cited alongside, same era.
Splicing forgeries localization through the use of first digit features,
I. Amerini, R. Becarelli, R. Caldelli, A. Del Mastio, · 2014
Cited alongside, same era.
Deep learning for steganalysis via convolutional neural networks,
Y. Qian, J. Dong, W. Wang, T. Tan, · 2015
Cited alongside, same era.
Median filtering forensics based on convolutional neural networks,
C. Jiansheng, K. Xiangui, L. Ye, Z. J. Wang, · 2015
Cited alongside, same era.
RAISE: A raw images dataset for digital image forensics,
D.-T. Dang-Nguyen, C. Pasquini, V. Conotter, G. Boato, · 2015
Cited alongside, same era.
Multi-scale fusion for improved localization of malicious tampering in digital images,
P. Korus, J. Huang, · 2016
Cited alongside, same era.
B. Bayar, M. C. Stamm, · 2016
Later among the works it cites.
Camera model identification with the use of deep convolutional neural networks,
A. Tuama, F. Comby, M. Chaumont, · 2016
Later among the works it cites.
First steps toward camera model identification with convolutional neural networks,
L. Bondi, L. Baroffio, D. Guera, P. Bestagini, E. J. Delp, S. Tubaro, · 2017
Closest in time.
Histogram layer, moving convolutional neural networks towards feature-based steganalysis,
V. Sedighi, J. Fridrich, · 2017
Closest in time.
A preliminary study on convolutional neural networks for camera model identification,
L. Bondi, D. Guera, L. Baroffio, P. Bestagini, E. J. Delp, S. Tubaro, · 2017
Closest in time.
Higher-order, adversary-aware, double jpeg-detection via selected training on attacked samples,
M. Barni, E. Nowroozi, B. Tondi, · 2017
Closest in time.