144 research outputs found
DKiS: Decay weight invertible image steganography with private key
Image steganography, defined as the practice of concealing information within
another image, traditionally encounters security challenges when its methods
become publicly known or are under attack. To address this, a novel private
key-based image steganography technique has been introduced. This approach
ensures the security of the hidden information, as access requires a
corresponding private key, regardless of the public knowledge of the
steganography method. Experimental evidence has been presented, demonstrating
the effectiveness of our method and showcasing its real-world applicability.
Furthermore, a critical challenge in the invertible image steganography process
has been identified by us: the transfer of non-essential, or `garbage',
information from the secret to the host pipeline. To tackle this issue, the
decay weight has been introduced to control the information transfer,
effectively filtering out irrelevant data and enhancing the performance of
image steganography. The code for this technique is publicly accessible at
https://github.com/yanghangAI/DKiS, and a practical demonstration can be found
at http://yanghang.site/hidekey
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/
GP-NAS-ensemble: a model for NAS Performance Prediction
It is of great significance to estimate the performance of a given model
architecture without training in the application of Neural Architecture Search
(NAS) as it may take a lot of time to evaluate the performance of an
architecture. In this paper, a novel NAS framework called GP-NAS-ensemble is
proposed to predict the performance of a neural network architecture with a
small training dataset. We make several improvements on the GP-NAS model to
make it share the advantage of ensemble learning methods. Our method ranks
second in the CVPR2022 second lightweight NAS challenge performance prediction
track
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