112 research outputs found
StegNet: Mega Image Steganography Capacity with Deep Convolutional Network
Traditional image steganography often leans interests towards safely
embedding hidden information into cover images with payload capacity almost
neglected. This paper combines recent deep convolutional neural network methods
with image-into-image steganography. It successfully hides the same size images
with a decoding rate of 98.2% or bpp (bits per pixel) of 23.57 by changing only
0.76% of the cover image on average. Our method directly learns end-to-end
mappings between the cover image and the embedded image and between the hidden
image and the decoded image. We~further show that our embedded image, while
with mega payload capacity, is still robust to statistical analysis.Comment: https://github.com/adamcavendish/StegNet-Mega-Image-Steganography-Capacity-with-Deep-Convolutional-Networ
Data Hiding with Deep Learning: A Survey Unifying Digital Watermarking and Steganography
Data hiding is the process of embedding information into a noise-tolerant
signal such as a piece of audio, video, or image. Digital watermarking is a
form of data hiding where identifying data is robustly embedded so that it can
resist tampering and be used to identify the original owners of the media.
Steganography, another form of data hiding, embeds data for the purpose of
secure and secret communication. This survey summarises recent developments in
deep learning techniques for data hiding for the purposes of watermarking and
steganography, categorising them based on model architectures and noise
injection methods. The objective functions, evaluation metrics, and datasets
used for training these data hiding models are comprehensively summarised.
Finally, we propose and discuss possible future directions for research into
deep data hiding techniques
PRIS: Practical robust invertible network for image steganography
Image steganography is a technique of hiding secret information inside
another image, so that the secret is not visible to human eyes and can be
recovered when needed. Most of the existing image steganography methods have
low hiding robustness when the container images affected by distortion. Such as
Gaussian noise and lossy compression. This paper proposed PRIS to improve the
robustness of image steganography, it based on invertible neural networks, and
put two enhance modules before and after the extraction process with a 3-step
training strategy. Moreover, rounding error is considered which is always
ignored by existing methods, but actually it is unavoidable in practical. A
gradient approximation function (GAF) is also proposed to overcome the
undifferentiable issue of rounding distortion. Experimental results show that
our PRIS outperforms the state-of-the-art robust image steganography method in
both robustness and practicability. Codes are available at
https://github.com/yanghangAI/PRIS, demonstration of our model in practical at
http://yanghang.site/hide/
- …