-
1
-
-
34247562352
-
Steganalysis based on Markov Model of thresholded prediction-error image
-
Toronto, ON, Canada, Jul. 9-12
-
D. Zou, Y. Q. Shi, W. Su, and G. Xuan, "Steganalysis based on Markov Model of thresholded prediction-error image," in Proc. IEEE Int. Conf. Multimedia Expo, Toronto, ON, Canada, Jul. 9-12, 2006, pp. 1365-1368.
-
(2006)
Proc. IEEE Int. Conf. Multimedia Expo
, pp. 1365-1368
-
-
Zou, D.1
Shi, Y.Q.2
Su, W.3
Xuan, G.4
-
2
-
-
84874405772
-
Textural features for steganalysis
-
Berkeley, CA, USA, May
-
Y. Q. Shi, P. Sutthiwan, and L. Chen, "Textural features for steganalysis," in Proc. 14th Int. Conf. Inf. Hiding, Berkeley, CA, USA, May 2012, vol. 7692, pp. 63-77.
-
(2012)
Proc. 14th Int. Conf. Inf. Hiding
, vol.7692
, pp. 63-77
-
-
Shi, Y.Q.1
Sutthiwan, P.2
Chen, L.3
-
3
-
-
84860434682
-
Rich models for steganalysis of digital images
-
Jun.
-
J. Fridrich and J. Kodovský, "Rich models for steganalysis of digital images," IEEE Trans. Inf. Forensics Security, vol. 7, no. 3, pp. 868-882, Jun. 2012.
-
(2012)
IEEE Trans. Inf. Forensics Security
, vol.7
, Issue.3
, pp. 868-882
-
-
Fridrich, J.1
Kodovský, J.2
-
4
-
-
77952641834
-
Steganalysis by subtractive pixel adjacency matrix
-
Jun.
-
T. Pevný, P. Bas, and J. Fridrich, "Steganalysis by subtractive pixel adjacency matrix," IEEE Trans. Inf. Forensics Security, vol. 5, no. 2, pp. 215-224, Jun. 2010.
-
(2010)
IEEE Trans. Inf. Forensics Security
, vol.5
, Issue.2
, pp. 215-224
-
-
Pevný, T.1
Bas, P.2
Fridrich, J.3
-
5
-
-
84888323613
-
Random projections of residuals for digital image steg analysis
-
Dec.
-
V. Holub and J. Fridrich, "Random projections of residuals for digital image steg analysis," IEEE Trans. Inf. Forensics Security, vol. 8, no. 12, pp. 1996-2006, Dec. 2013.
-
(2013)
IEEE Trans. Inf. Forensics Security
, vol.8
, Issue.12
, pp. 1996-2006
-
-
Holub, V.1
Fridrich, J.2
-
6
-
-
84983591266
-
Variable multi-dimensional co-occurrence histogram for steganalysis
-
Oct.
-
L. Chen, Y. Q. Shi, and P. Sutthiwan, "Variable multi-dimensional co-occurrence histogram for steganalysis," in Proc. IWDW, Oct. 2014, vol. 9023, pp. 559-573.
-
(2014)
Proc. IWDW
, vol.9023
, pp. 559-573
-
-
Chen, L.1
Shi, Y.Q.2
Sutthiwan, P.3
-
7
-
-
78549231320
-
Using high-dimensional image models to perform highly undetectable steganography
-
New York, NY, USA: Springer-Verlag, Jun. 28-30
-
T. Pevný, T. Filler, and P. Bas, "Using high-dimensional image models to perform highly undetectable steganography," in Proc. 12th Int. Workshop Inf. Hiding, New York, NY, USA: Springer-Verlag, Jun. 28-30, 2010, vol. 6387, pp. 161-177.
-
(2010)
Proc. 12th Int. Workshop Inf. Hiding
, vol.6387
, pp. 161-177
-
-
Pevný, T.1
Filler, T.2
Bas, P.3
-
9
-
-
84894039003
-
Universal distortion function for steganography in an arbitrary domain
-
V. Holub, J. Fridrich, and T. Denemark, "Universal distortion function for steganography in an arbitrary domain," EURASIP J. Inf. Secur., vol. 2014, no. 1, pp. 1-13, 2014.
-
(2014)
EURASIP J. Inf. Secur.
, vol.2014
, Issue.1
, pp. 1-13
-
-
Holub, V.1
Fridrich, J.2
Denemark, T.3
-
10
-
-
85015091451
-
Content-adaptive steganography by minimizing statistical detectability
-
Feb.
-
V. Sedighi, R. Cogranne, and J. Fridrich, "Content-adaptive steganography by minimizing statistical detectability," IEEE Trans. Inf. Forensics Security, vol. 11, no. 2, pp. 221-234, Feb. 2016.
-
(2016)
IEEE Trans. Inf. Forensics Security
, vol.11
, Issue.2
, pp. 221-234
-
-
Sedighi, V.1
Cogranne, R.2
Fridrich, J.3
-
11
-
-
84983146156
-
A new cost function for spatial image steganography
-
Paris, France, Oct.
-
B. Li, M. Wang, J. Huang, and X. Li, "A new cost function for spatial image steganography," in Proc. IEEE Int. Conf. Image Process. (ICIP), Paris, France, Oct. 2014, pp. 4206-4210.
-
(2014)
Proc. IEEE Int. Conf. Image Process. (ICIP)
, pp. 4206-4210
-
-
Li, B.1
Wang, M.2
Huang, J.3
Li, X.4
-
12
-
-
84904685564
-
Investigation on cost assignment in spatial image steganography
-
Aug.
-
B. Li, S. Tan, M. Wang, and J. Huang, "Investigation on cost assignment in spatial image steganography," IEEE Trans. Inf. Forensics Security, vol. 9, no. 8, pp. 1264-1277, Aug. 2014.
-
(2014)
IEEE Trans. Inf. Forensics Security
, vol.9
, Issue.8
, pp. 1264-1277
-
-
Li, B.1
Tan, S.2
Wang, M.3
Huang, J.4
-
13
-
-
0000494467
-
Handwritten digit recognition with a backpropagation neural network
-
D. S. Touretzky, Ed. San Mateo, CA, USA: Morgan Kaufmann
-
Y. Le Cun et al., "Handwritten digit recognition with a backpropagation neural network," in Advances in Neural Information Processing Systems 2, D. S. Touretzky, Ed. San Mateo, CA, USA: Morgan Kaufmann, 1990, pp. 396-404.
-
(1990)
Advances in Neural Information Processing Systems 2
, pp. 396-404
-
-
Le Cun, Y.1
-
14
-
-
84876231242
-
Imagenet classification with deep convolutional neural networks
-
A. Krizhevsky, I. Sutskever, and G. Hinton, "Imagenet classification with deep convolutional neural networks," in Proc. Adv. Neural Inf. Process. Syst., 2012, pp. 1106-1114.
-
(2012)
Proc. Adv. Neural Inf. Process. Syst.
, pp. 1106-1114
-
-
Krizhevsky, A.1
Sutskever, I.2
Hinton, G.3
-
15
-
-
84947041871
-
ImageNet large scale visual recognition challenge
-
O. Russakovsky et al., "ImageNet large scale visual recognition challenge," Int. J. Comput. Vis., vol. 115, pp. 211-252, 2015.
-
(2015)
Int. J. Comput. Vis.
, vol.115
, pp. 211-252
-
-
Russakovsky, O.1
-
18
-
-
84958589374
-
-
arXiv:1512.03385
-
K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," 2015, arXiv:1512.03385.
-
(2015)
Deep Residual Learning for Image Recognition
-
-
He, K.1
Zhang, X.2
Ren, S.3
Sun, J.4
-
19
-
-
84933053045
-
Median filtering forensics based on convolutional neural networks
-
Nov.
-
J. Chen, X. Kang, Y. Liu, and Z. J. Wang, "Median filtering forensics based on convolutional neural networks," IEEE Signal Process. Lett., vol. 22, no. 11, pp. 1849-1853, Nov. 2015.
-
(2015)
IEEE Signal Process. Lett.
, vol.22
, Issue.11
, pp. 1849-1853
-
-
Chen, J.1
Kang, X.2
Liu, Y.3
Wang, Z.J.4
-
21
-
-
84932146885
-
Deep learning for steganalysis via convolutional neural networks
-
Y. Qian, J. Dong, W. Wang, and T. Tan, "Deep learning for steganalysis via convolutional neural networks," in Proc. SPIE Media Watermarking Secur. Forensics, 2015, vol. 9409, pp. 94090J-94090J-10.
-
(2015)
Proc. SPIE Media Watermarking Secur. Forensics
, vol.9409
, pp. 94090J-94090J10
-
-
Qian, Y.1
Dong, J.2
Wang, W.3
Tan, T.4
-
22
-
-
80053005547
-
Break our steganographic system- The ins and outs of organizing BOSS
-
May
-
P. Bas, T. Filler, and T. Pevný, "Break our steganographic system- The ins and outs of organizing BOSS," in Proc. Inf. Hiding, May 2011, vol. 6958, pp. 59-70.
-
(2011)
Proc. Inf. Hiding
, vol.6958
, pp. 59-70
-
-
Bas, P.1
Filler, T.2
Pevný, T.3
-
23
-
-
42949088538
-
Revisiting weighted stego-image steganalysis
-
San Jose, CA, USA, Jan. 27-31
-
A. D. Ker, R. Böhme, E. J. Delp, and P. W. Wong, Eds., "Revisiting weighted stego-image steganalysis," in Proc. SPIE, Electron. Imag. Secur. Forensics Steganogr. Watermark. Multimedia Contents X, San Jose, CA, USA, Jan. 27-31, 2008, vol. 6819, pp. 5.1-5.17.
-
(2008)
Proc. SPIE, Electron. Imag. Secur. Forensics Steganogr. Watermark. Multimedia Contents X
, vol.6819
, pp. 51-517
-
-
Ker, A.D.1
Böhme, R.2
Delp, E.J.3
Wong, P.W.4
-
24
-
-
77956509090
-
Rectified linear units improve restricted Boltzmann machines
-
V. Nair and G. E. Hinton, "Rectified linear units improve restricted Boltzmann machines," in Proc. Int. Conf. Mach. Learn., 2010, pp. 807- 814.
-
(2010)
Proc. Int. Conf. Mach. Learn.
, pp. 807-814
-
-
Nair, V.1
Hinton, G.E.2
-
25
-
-
84858263654
-
Ensemble classifiers for steganalysis of digital media
-
Apr.
-
J. Kodovsky, J. Fridrich, and V. Holub, "Ensemble classifiers for steganalysis of digital media," IEEE Trans. Inf. Forensics Security, vol. 7, no. 2, pp. 432-444, Apr. 2012.
-
(2012)
IEEE Trans. Inf. Forensics Security
, vol.7
, Issue.2
, pp. 432-444
-
-
Kodovsky, J.1
Fridrich, J.2
Holub, V.3
-
26
-
-
84964941647
-
Deep learning is a good steganalysis tool when embedding key is reused for different images, even if there is a cover source mismatch
-
Feb.
-
L. Pibre, J. Pasquet, D. Ienco, and M. Chaumont, "Deep learning is a good steganalysis tool when embedding key is reused for different images, even if there is a cover source mismatch," in Proc. SPIE, Feb. 2016, pp. 14-18.
-
(2016)
Proc. SPIE
, pp. 14-18
-
-
Pibre, L.1
Pasquet, J.2
Ienco, D.3
Chaumont, M.4
-
27
-
-
84862277874
-
Understanding the difficulty of training deep feedforward neural networks
-
X. Glorot and Y. Bengio, "Understanding the difficulty of training deep feedforward neural networks," in Proc. Artif. Intell. Statist. (AISTATS), 2010, pp. 249-256.
-
(2010)
Proc. Artif. Intell. Statist. (AISTATS)
, pp. 249-256
-
-
Glorot, X.1
Bengio, Y.2
-
28
-
-
84913580146
-
Caffe: Convolutional architecture for fast feature embedding
-
Y. Jia et al., "Caffe: Convolutional architecture for fast feature embedding," in Proc. ACM Int. Conf. Multimedia, 2014, pp. 675-678.
-
(2014)
Proc. ACM Int. Conf. Multimedia
, pp. 675-678
-
-
Jia, Y.1
-
29
-
-
84904163933
-
Dropout: A simple way to prevent neural networks from overfitting
-
Jan.
-
N. Srivastava, G. E. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, "Dropout: A simple way to prevent neural networks from overfitting," J. Mach. Learn. Res., vol. 15, no. 1, pp. 1929-1958, Jan. 2014.
-
(2014)
J. Mach. Learn. Res.
, vol.15
, Issue.1
, pp. 1929-1958
-
-
Srivastava, N.1
Hinton, G.E.2
Krizhevsky, A.3
Sutskever, I.4
Salakhutdinov, R.5
-
30
-
-
0030211964
-
Bagging predictors
-
L. Breiman, "Bagging predictors," Mach. Learn., vol. 24, no. 2, pp. 123- 140, 1996.
-
(1996)
Mach. Learn.
, vol.24
, Issue.2
, pp. 123-140
-
-
Breiman, L.1
-
31
-
-
84929231486
-
Selection-channel-aware rich model for steganalysis of digital images
-
T. Denemark, V. Sedighi, V. Holub, R. Cogranne, and J. Fridrich, "Selection-channel-aware rich model for steganalysis of digital images," in Proc. IEEE Int. Workshop Inf. Forensics Security (WIFS), 2014, pp. 48-53.
-
(2014)
Proc. IEEE Int. Workshop Inf. Forensics Security (WIFS)
, pp. 48-53
-
-
Denemark, T.1
Sedighi, V.2
Holub, V.3
Cogranne, R.4
Fridrich, J.5
-
32
-
-
84927741168
-
Adaptive steganalysis against WOW embedding algorithm
-
Jun.
-
W. Tang, H. Li, W. Luo, and J. Huang, "Adaptive steganalysis against WOW embedding algorithm," in Proc. ACM Workshop Inf. Hiding Multimedia Secur. (IH&MMSec), Jun. 2014, pp. 91-96.
-
(2014)
Proc. ACM Workshop Inf. Hiding Multimedia Secur. (IH&MMSec)
, pp. 91-96
-
-
Tang, W.1
Li, H.2
Luo, W.3
Huang, J.4
-
33
-
-
84959219306
-
Adaptive steganalysis based on embedding probabilities of pixels
-
Apr.
-
W. Tang, H. Li, W. Luo, and J. Huang, "Adaptive steganalysis based on embedding probabilities of pixels," IEEE Trans. Inf. Forensics Security, vol. 11, no. 4, pp. 734-745, Apr. 2016.
-
(2016)
IEEE Trans. Inf. Forensics Security
, vol.11
, Issue.4
, pp. 734-745
-
-
Tang, W.1
Li, H.2
Luo, W.3
Huang, J.4
-
34
-
-
84964941670
-
Improving selection channel- aware steganalysis features
-
Feb.
-
T. Denemark, J. Fridrich, and P. Comesaña-Alfaro, "Improving selection channel- aware steganalysis features," in Proc. SPIE, Feb. 2016, pp. 14- 18.
-
(2016)
Proc. SPIE
, pp. 14-18
-
-
Denemark, T.1
Fridrich, J.2
Comesaña-Alfaro, P.3
-
35
-
-
84920768177
-
Low-complexity features for JPEG steganalysis using undecimated DCT
-
Feb.
-
V. Holub and J. Fridrich, "Low-complexity features for JPEG steganalysis using undecimated DCT," IEEE Trans. Inf. Forensics Security, vol. 10, no. 2, pp. 219-228, Feb. 2015.
-
(2015)
IEEE Trans. Inf. Forensics Security
, vol.10
, Issue.2
, pp. 219-228
-
-
Holub, V.1
Fridrich, J.2
-
37
-
-
84962894302
-
Steganalysis of adaptive JPEG steganography using 2D Gabor filters
-
X. Song, F. Liu, C. Yang, X. Luo, and Y. Zhang, "Steganalysis of adaptive JPEG steganography using 2D Gabor filters," in Proc. ACM Workshop Inf. Hiding Multimedia Secur. (IH&MMSec), 2015, pp. 15-23.
-
(2015)
Proc. ACM Workshop Inf. Hiding Multimedia Secur. (IH&MMSec)
, pp. 15-23
-
-
Song, X.1
Liu, F.2
Yang, C.3
Luo, X.4
Zhang, Y.5
|