161 research outputs found
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
Double-Flow-based Steganography without Embedding for Image-to-Image Hiding
As an emerging concept, steganography without embedding (SWE) hides a secret
message without directly embedding it into a cover. Thus, SWE has the unique
advantage of being immune to typical steganalysis methods and can better
protect the secret message from being exposed. However, existing SWE methods
are generally criticized for their poor payload capacity and low fidelity of
recovered secret messages. In this paper, we propose a novel
steganography-without-embedding technique, named DF-SWE, which addresses the
aforementioned drawbacks and produces diverse and natural stego images.
Specifically, DF-SWE employs a reversible circulation of double flow to build a
reversible bijective transformation between the secret image and the generated
stego image. Hence, it provides a way to directly generate stego images from
secret images without a cover image. Besides leveraging the invertible
property, DF-SWE can invert a secret image from a generated stego image in a
nearly lossless manner and increases the fidelity of extracted secret images.
To the best of our knowledge, DF-SWE is the first SWE method that can hide
large images and multiple images into one image with the same size,
significantly enhancing the payload capacity. According to the experimental
results, the payload capacity of DF-SWE achieves 24-72 BPP is 8000-16000 times
compared to its competitors while producing diverse images to minimize the
exposure risk. Importantly, DF-SWE can be applied in the steganography of
secret images in various domains without requiring training data from the
corresponding domains. This domain-agnostic property suggests that DF-SWE can
1) be applied to hiding private data and 2) be deployed in resource-limited
systems
Deep Learning for Reversible Steganography: Principles and Insights
Deep-learning\textendash{centric} reversible steganography has emerged as a
promising research paradigm. A direct way of applying deep learning to
reversible steganography is to construct a pair of encoder and decoder, whose
parameters are trained jointly, thereby learning the steganographic system as a
whole. This end-to-end framework, however, falls short of the reversibility
requirement because it is difficult for this kind of monolithic system, as a
black box, to create or duplicate intricate reversible mechanisms. In response
to this issue, a recent approach is to carve up the steganographic system and
work on modules independently. In particular, neural networks are deployed in
an analytics module to learn the data distribution, while an established
mechanism is called upon to handle the remaining tasks. In this paper, we
investigate the modular framework and deploy deep neural networks in a
reversible steganographic scheme referred to as prediction-error modulation, in
which an analytics module serves the purpose of pixel intensity prediction. The
primary focus of this study is on deep-learning\textendash{based} context-aware
pixel intensity prediction. We address the unsolved issues reported in related
literature, including the impact of pixel initialisation on prediction accuracy
and the influence of uncertainty propagation in dual-layer embedding.
Furthermore, we establish a connection between context-aware pixel intensity
prediction and low-level computer vision and analyse the performance of several
advanced neural networks
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