Digital image steganography survey and investigation (goal, assessment, method, development, and dataset)
Digital steganography has a long history, starting to be developed in the 90s until now. The
main aspects of early steganography are security, imperceptibility, and payload. Security is …
main aspects of early steganography are security, imperceptibility, and payload. Security is …
Challenges and opportunities of deep learning-based process fault detection and diagnosis: a review
J Yu, Y Zhang - Neural Computing and Applications, 2023 - Springer
Process fault detection and diagnosis (FDD) is a predominant task to ensure product quality
and process reliability in modern industrial systems. Those traditional FDD techniques are …
and process reliability in modern industrial systems. Those traditional FDD techniques are …
Hinet: Deep image hiding by invertible network
Image hiding aims to hide a secret image into a cover image in an imperceptible way, and
then recover the secret image perfectly at the receiver end. Capacity, invisibility and security …
then recover the secret image perfectly at the receiver end. Capacity, invisibility and security …
DeepMIH: Deep invertible network for multiple image hiding
Multiple image hiding aims to hide multiple secret images into a single cover image, and
then recover all secret images perfectly. Such high-capacity hiding may easily lead to …
then recover all secret images perfectly. Such high-capacity hiding may easily lead to …
Channel attention image steganography with generative adversarial networks
J Tan, X Liao, J Liu, Y Cao… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Recently, extensive research has revealed the enormous potential of deep learning in the
application of image steganography. However, some defects still exist in previous studies on …
application of image steganography. However, some defects still exist in previous studies on …
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 …
Are GAN generated images easy to detect? A critical analysis of the state-of-the-art
The advent of deep learning has brought a significant improvement in the quality of
generated media. However, with the increased level of photorealism, synthetic media are …
generated media. However, with the increased level of photorealism, synthetic media are …
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 …
An embedding cost learning framework using GAN
Successful adaptive steganography has mainly focused on embedding the payload while
minimizing an appropriately defined distortion function. The application of deep learning to …
minimizing an appropriately defined distortion function. The application of deep learning to …