254 research outputs found
Digital Cardan Grille: A Modern Approach for Information Hiding
In this paper, a new framework for construction of Cardan grille for
information hiding is proposed. Based on the semantic image inpainting
technique, the stego image are driven by secret messages directly. A mask
called Digital Cardan Grille (DCG) for determining the hidden location is
introduced to hide the message. The message is written to the corrupted region
that needs to be filled in the corrupted image in advance. Then the corrupted
image with secret message is feeded into a Generative Adversarial Network (GAN)
for semantic completion. The adversarial game not only reconstruct the
corrupted image , but also generate a stego image which contains the logic
rationality of image content. The experimental results verify the feasibility
of the proposed method
Generative Steganography with Kerckhoffs' Principle based on Generative Adversarial Networks
The distortion in steganography that usually comes from the modification or
recoding on the cover image during the embedding process leaves the
steganalyzer with possibility of discriminating. Faced with such a risk, we
propose generative steganography with Kerckhoffs' principle (GSK) in this
letter. In GSK, the secret messages are generated by a cover image using a
generator rather than embedded into the cover, thus resulting in no
modifications in the cover. To ensure the security, the generators are trained
to meet Kerckhoffs' principle based on generative adversarial networks (GAN).
Everything about the GSK system, except the extraction key, is public knowledge
for the receivers. The secret messages can be outputted by the generator if and
only if the extraction key and the cover image are both inputted. In the
generator training procedures, there are two GANs, Message- GAN and Cover-GAN,
designed to work jointly making the generated results under the control of the
extraction key and the cover image. We provide experimental results on the
training process and give an example of the working process by adopting a
generator trained on MNIST, which demonstrate that GSK can use a cover image
without any modification to generate messages, and without the extraction key
or the cover image, only meaningless results would be obtained
Generative Reversible Data Hiding by Image to Image Translation via GANs
The traditional reversible data hiding technique is based on cover image
modification which inevitably leaves some traces of rewriting that can be more
easily analyzed and attacked by the warder. Inspired by the cover synthesis
steganography based generative adversarial networks, in this paper, a novel
generative reversible data hiding scheme (GRDH) by image translation is
proposed. First, an image generator is used to obtain a realistic image, which
is used as an input to the image-to-image translation model with CycleGAN.
After image translation, a stego image with different semantic information will
be obtained. The secret message and the original input image can be recovered
separately by a well-trained message extractor and the inverse transform of the
image translation. Experimental results have verified the effectiveness of the
scheme
Coverless Information Hiding Based on Generative adversarial networks
Traditional image steganography modifies the content of the image more or
less, it is hard to resist the detection of image steganalysis tools. To
address this problem, a novel method named generative coverless information
hiding method based on generative adversarial networks is proposed in this
paper. The main idea of the method is that the class label of generative
adversarial networks is replaced with the secret information as a driver to
generate hidden image directly, and then extract the secret information from
the hidden image through the discriminator. It's the first time that the
coverless information hiding is achieved by generative adversarial networks.
Compared with the traditional image steganography, this method does not modify
the content of the original image. therefore, this method can resist image
steganalysis tools effectively. In terms of steganographic capacity,
anti-steganalysis, safety and reliability, the experimen shows that this hidden
algorithm performs well.Comment: arXiv admin note: text overlap with arXiv:1703.05502 by other author
When Provably Secure Steganography Meets Generative Models
Steganography is the art and science of hiding secret messages in public
communication so that the presence of the secret messages cannot be detected.
There are two provably secure steganographic frameworks, one is black-box
sampling based and the other is compression based. The former requires a
perfect sampler which yields data following the same distribution, and the
latter needs explicit distributions of generative objects. However, these two
conditions are too strict even unrealistic in the traditional data environment,
because it is hard to model the explicit distribution of natural image. With
the development of deep learning, generative models bring new vitality to
provably secure steganography, which can serve as the black-box sampler or
provide the explicit distribution of generative media. Motivated by this, this
paper proposes two types of provably secure stegosystems with generative
models. Specifically, we first design block-box sampling based provably secure
stegosystem for broad generative models without explicit distribution, such as
GAN, VAE, and flow-based generative models, where the generative network can
serve as the perfect sampler. For compression based stegosystem, we leverage
the generative models with explicit distribution such as autoregressive models
instead, where the adaptive arithmetic coding plays the role of the perfect
compressor, decompressing the encrypted message bits into generative media, and
the receiver can compress the generative media into the encrypted message bits.
To show the effectiveness of our method, we take DFC-VAE, Glow, WaveNet as
instances of generative models and demonstrate the perfectly secure performance
of these stegosystems with the state-of-the-art steganalysis methods
How Generative Adversarial Networks and Their Variants Work: An Overview
Generative Adversarial Networks (GAN) have received wide attention in the
machine learning field for their potential to learn high-dimensional, complex
real data distribution. Specifically, they do not rely on any assumptions about
the distribution and can generate real-like samples from latent space in a
simple manner. This powerful property leads GAN to be applied to various
applications such as image synthesis, image attribute editing, image
translation, domain adaptation and other academic fields. In this paper, we aim
to discuss the details of GAN for those readers who are familiar with, but do
not comprehend GAN deeply or who wish to view GAN from various perspectives. In
addition, we explain how GAN operates and the fundamental meaning of various
objective functions that have been suggested recently. We then focus on how the
GAN can be combined with an autoencoder framework. Finally, we enumerate the
GAN variants that are applied to various tasks and other fields for those who
are interested in exploiting GAN for their research.Comment: 41 pages, 16 figures, Published in ACM Computing Surveys (CSUR
Image Disguise based on Generative Model
To protect image contents, most existing encryption algorithms are designed
to transform an original image into a texture-like or noise-like image, which
is, however, an obvious visual sign indicating the presence of an encrypted
image, results in a significantly large number of attacks. To solve this
problem, in this paper, we propose a new image encryption method to generate a
visually same image as the original one by sending a meaning-normal and
independent image to a corresponding well-trained generative model to achieve
the effect of disguising the original image. This image disguise method not
only solves the problem of obvious visual implication, but also guarantees the
security of the information.Comment: 4 pages,9 figure
Steganography GAN: Cracking Steganography with Cycle Generative Adversarial Networks
For as long as humans have participated in the act of communication,
concealing information in those communicative mediums has manifested into an
art of its own. Crytographic messages, through written language or images, are
a means of concealment, usually reserved for highly sensitive or compromising
information. Specifically, the field of Cryptography is the construction and
analysis of protocols that prevent third parties from understanding private
messages. Steganography is related to Cryptography in that the goal is to
obscure information using some method or algorithm, but the most important
difference is that the information and the method of concealing information
within Steganography both involve images--more precisely, the embedding of one
image or piece of information into another image. Ever since the creation of
covert communication methods, steps have been taken to crack cryptography and
steganography algorithms. The desire for this rises from both human curiosity
and the need to counteract adverse uses, such as encoding harmful media in
inconspicuous media (phishing attack). In this paper, we succeed in cracking
the Least Significant Bit (LSB) steganography algorithm using Cycle Generative
Adversarial Networks (CycleGANs) and Bayesian Optimization and compare the use
of CycleGANs against Convolutional Autoencoders. The results of our experiments
highlight the promising nature of CycleGANs in cracking steganography and open
several possible avenues of research
Recent Advances of Image Steganography with Generative Adversarial Networks
In the past few years, the Generative Adversarial Network (GAN) which
proposed in 2014 has achieved great success. GAN has achieved many research
results in the field of computer vision and natural language processing. Image
steganography is dedicated to hiding secret messages in digital images, and has
achieved the purpose of covert communication. Recently, research on image
steganography has demonstrated great potential for using GAN and neural
networks. In this paper we review different strategies for steganography such
as cover modification, cover selection and cover synthesis by GANs, and discuss
the characteristics of these methods as well as evaluation metrics and provide
some possible future research directions in image steganography.Comment: 39 pages, 26 figure
Synchronized Detection and Recovery of Steganographic Messages with Adversarial Learning
In this work, we mainly study the mechanism of learning the steganographic
algorithm as well as combining the learning process with adversarial learning
to learn a good steganographic algorithm. To handle the problem of embedding
secret messages into the specific medium, we design a novel adversarial modules
to learn the steganographic algorithm, and simultaneously train three modules
called generator, discriminator and steganalyzer. Different from existing
methods, the three modules are formalized as a game to communicate with each
other. In the game, the generator and discriminator attempt to communicate with
each other using secret messages hidden in an image. While the steganalyzer
attempts to analyze whether there is a transmission of confidential
information. We show that through unsupervised adversarial training, the
adversarial model can produce robust steganographic solutions, which act like
an encryption. Furthermore, we propose to utilize supervised adversarial
training method to train a robust steganalyzer, which is utilized to
discriminate whether an image contains secret information. Numerous experiments
are conducted on publicly available dataset to demonstrate the effectiveness of
the proposed method
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