Steganalyzing Images of Arbitrary Size with CNNs
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[1] Jing Dong,et al. Learning and transferring representations for image steganalysis using convolutional neural network , 2016, 2016 IEEE International Conference on Image Processing (ICIP).
[2] Jessica J. Fridrich,et al. Designing steganographic distortion using directional filters , 2012, 2012 IEEE International Workshop on Information Forensics and Security (WIFS).
[3] Jing Dong,et al. Deep learning for steganalysis via convolutional neural networks , 2015, Electronic Imaging.
[4] Jiaxing Zhang,et al. Scale-Invariant Convolutional Neural Networks , 2014, ArXiv.
[5] Tomás Pevný,et al. The square root law of steganographic capacity , 2008, MM&Sec '08.
[6] Tomás Pevný,et al. Optimizing pooling function for pooled steganalysis , 2015, 2015 IEEE International Workshop on Information Forensics and Security (WIFS).
[7] Jessica J. Fridrich,et al. Practical strategies for content-adaptive batch steganography and pooled steganalysis , 2017, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[8] Tomás Pevný,et al. "Break Our Steganographic System": The Ins and Outs of Organizing BOSS , 2011, Information Hiding.
[9] Jessica J. Fridrich,et al. Ensemble Classifiers for Steganalysis of Digital Media , 2012, IEEE Transactions on Information Forensics and Security.
[10] Guanshuo Xu,et al. Deep Convolutional Neural Network to Detect J-UNIWARD , 2017, IH&MMSec.
[11] Yun Q. Shi,et al. Structural Design of Convolutional Neural Networks for Steganalysis , 2016, IEEE Signal Processing Letters.
[12] Tomás Pevný,et al. Batch steganography in the real world , 2012, MM&Sec '12.
[13] Jiangqun Ni,et al. Deep Learning Hierarchical Representations for Image Steganalysis , 2017, IEEE Transactions on Information Forensics and Security.
[14] J. Nadal,et al. Self-Similarity Properties of Natural Images Resemble Those of Turbulent Flows , 1998, cond-mat/0107314.
[15] N. Parga,et al. Ju l 2 00 1 The multi-fractal structure of contrast changes in natural images : from sharp edges to textures , 2008 .
[16] Mo Chen,et al. JPEG-Phase-Aware Convolutional Neural Network for Steganalysis of JPEG Images , 2017, IH&MMSec.
[17] Jessica J. Fridrich,et al. Rich Models for Steganalysis of Digital Images , 2012, IEEE Transactions on Information Forensics and Security.
[18] Andrew D. Ker. Batch Steganography and Pooled Steganalysis , 2006, Information Hiding.
[19] Matthew D. Zeiler. ADADELTA: An Adaptive Learning Rate Method , 2012, ArXiv.
[20] Andrew D. Ker. Locally Square Distortion and Batch Steganographic Capacity , 2009, Int. J. Digit. Crime Forensics.
[21] Andrew D. Ker. Perturbation Hiding and the Batch Steganography Problem , 2008, Information Hiding.
[22] Yun Q. Shi,et al. Ensemble of CNNs for Steganalysis: An Empirical Study , 2016, IH&MMSec.
[23] Andrew D. Ker,et al. Steganalysis with mismatched covers: do simple classifiers help? , 2012, MM&Sec '12.
[24] Rémi Cogranne,et al. A sequential method for online steganalysis , 2015, 2015 IEEE International Workshop on Information Forensics and Security (WIFS).
[25] Andrew D. Ker. Batch steganography and the threshold game , 2007, Electronic Imaging.
[26] Jessica J. Fridrich,et al. Steganalysis of JPEG images using rich models , 2012, Other Conferences.
[27] Jessica J. Fridrich,et al. The square root law of steganographic capacity for Markov covers , 2009, Electronic Imaging.
[28] Yi Zhang,et al. Steganalysis of Adaptive JPEG Steganography Using 2D Gabor Filters , 2015, IH&MMSec.
[29] Andrew D. Ker. A Capacity Result for Batch Steganography , 2007, IEEE Signal Processing Letters.
[30] Jessica Fridrich,et al. Modeling and Extending the Ensemble Classifier for Steganalysis of Digital Images Using Hypothesis Testing Theory , 2015, IEEE Transactions on Information Forensics and Security.
[31] Ruderman,et al. Multiscaling and information content of natural color images , 2000, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.