Deep residual network for steganalysis of digital images
M Boroumand, M Chen, J Fridrich - IEEE Transactions on …, 2018 - ieeexplore.ieee.org
Steganography detectors built as deep convolutional neural networks have firmly
established themselves as superior to the previous detection paradigm-classifiers based on …
established themselves as superior to the previous detection paradigm-classifiers based on …
Deep learning hierarchical representations for image steganalysis
J Ye, J Ni, Y Yi - IEEE Transactions on Information Forensics …, 2017 - ieeexplore.ieee.org
Nowadays, the prevailing detectors of steganographic communication in digital images
mainly consist of three steps, ie, residual computation, feature extraction, and binary …
mainly consist of three steps, ie, residual computation, feature extraction, and binary …
A Siamese CNN for image steganalysis
W You, H Zhang, X Zhao - IEEE Transactions on Information …, 2020 - ieeexplore.ieee.org
Image steganalysis is a technique for detecting data hidden in images. Recent research has
shown the powerful capabilities of using convolutional neural networks (CNN) for image …
shown the powerful capabilities of using convolutional neural networks (CNN) for image …
Depth-wise separable convolutions and multi-level pooling for an efficient spatial CNN-based steganalysis
For steganalysis, many studies showed that convolutional neural network (CNN) has better
performances than the two-part structure of traditional machine learning methods. Existing …
performances than the two-part structure of traditional machine learning methods. Existing …
Structural design of convolutional neural networks for steganalysis
Recent studies have indicated that the architectures of convolutional neural networks
(CNNs) tailored for computer vision may not be best suited to image steganalysis. In this …
(CNNs) tailored for computer vision may not be best suited to image steganalysis. In this …
An automatic cost learning framework for image steganography using deep reinforcement learning
Automatic cost learning for steganography based on deep neural networks is receiving
increasing attention. Steganographic methods under such a framework have been shown to …
increasing attention. Steganographic methods under such a framework have been shown to …
Content-adaptive steganography by minimizing statistical detectability
Most current steganographic schemes embed the secret payload by minimizing a
heuristically defined distortion. Similarly, their security is evaluated empirically using …
heuristically defined distortion. Similarly, their security is evaluated empirically using …
Selection-channel-aware rich model for steganalysis of digital images
From the perspective of signal detection theory, it seems obvious that knowing the
probabilities with which the individual cover elements are modified during message …
probabilities with which the individual cover elements are modified during message …
A strategy of clustering modification directions in spatial image steganography
Most of the recently proposed steganographic schemes are based on minimizing an additive
distortion function defined as the sum of embedding costs for individual pixels. In such an …
distortion function defined as the sum of embedding costs for individual pixels. In such an …
Stacked convolutional auto-encoders for steganalysis of digital images
In this paper, we point out that SRM (Spatial-domain Rich Model), the most successful
steganalysis framework of digital images possesses a similar architecture to CNN …
steganalysis framework of digital images possesses a similar architecture to CNN …