68 research outputs found
Ensuring message embedding in wet paper steganography
International audienceSyndrome coding has been proposed by Crandall in 1998 as a method to stealthily embed a message in a cover-medium through the use of bounded decoding. In 2005, Fridrich et al. introduced wet paper codes to improve the undetectability of the embedding by nabling the sender to lock some components of the cover-data, according to the nature of the cover-medium and the message. Unfortunately, almost all existing methods solving the bounded decoding syndrome problem with or without locked components have a non-zero probability to fail. In this paper, we introduce a randomized syndrome coding, which guarantees the embedding success with probability one. We analyze the parameters of this new scheme in the case of perfect codes
Perfectly Secure Steganography: Capacity, Error Exponents, and Code Constructions
An analysis of steganographic systems subject to the following perfect
undetectability condition is presented in this paper. Following embedding of
the message into the covertext, the resulting stegotext is required to have
exactly the same probability distribution as the covertext. Then no statistical
test can reliably detect the presence of the hidden message. We refer to such
steganographic schemes as perfectly secure. A few such schemes have been
proposed in recent literature, but they have vanishing rate. We prove that
communication performance can potentially be vastly improved; specifically, our
basic setup assumes independently and identically distributed (i.i.d.)
covertext, and we construct perfectly secure steganographic codes from public
watermarking codes using binning methods and randomized permutations of the
code. The permutation is a secret key shared between encoder and decoder. We
derive (positive) capacity and random-coding exponents for perfectly-secure
steganographic systems. The error exponents provide estimates of the code
length required to achieve a target low error probability. We address the
potential loss in communication performance due to the perfect-security
requirement. This loss is the same as the loss obtained under a weaker order-1
steganographic requirement that would just require matching of first-order
marginals of the covertext and stegotext distributions. Furthermore, no loss
occurs if the covertext distribution is uniform and the distortion metric is
cyclically symmetric; steganographic capacity is then achieved by randomized
linear codes. Our framework may also be useful for developing computationally
secure steganographic systems that have near-optimal communication performance.Comment: To appear in IEEE Trans. on Information Theory, June 2008; ignore
Version 2 as the file was corrupte
Perfectly Secure Steganography: Capacity, Error Exponents, and Code Constructions
An analysis of steganographic systems subject to the following perfect
undetectability condition is presented in this paper. Following embedding of
the message into the covertext, the resulting stegotext is required to have
exactly the same probability distribution as the covertext. Then no statistical
test can reliably detect the presence of the hidden message. We refer to such
steganographic schemes as perfectly secure. A few such schemes have been
proposed in recent literature, but they have vanishing rate. We prove that
communication performance can potentially be vastly improved; specifically, our
basic setup assumes independently and identically distributed (i.i.d.)
covertext, and we construct perfectly secure steganographic codes from public
watermarking codes using binning methods and randomized permutations of the
code. The permutation is a secret key shared between encoder and decoder. We
derive (positive) capacity and random-coding exponents for perfectly-secure
steganographic systems. The error exponents provide estimates of the code
length required to achieve a target low error probability. We address the
potential loss in communication performance due to the perfect-security
requirement. This loss is the same as the loss obtained under a weaker order-1
steganographic requirement that would just require matching of first-order
marginals of the covertext and stegotext distributions. Furthermore, no loss
occurs if the covertext distribution is uniform and the distortion metric is
cyclically symmetric; steganographic capacity is then achieved by randomized
linear codes. Our framework may also be useful for developing computationally
secure steganographic systems that have near-optimal communication performance.Comment: To appear in IEEE Trans. on Information Theory, June 2008; ignore
Version 2 as the file was corrupte
Improving success probability and embedding efficiency in code based steganography
For stegoschemes arising from error correcting codes, embedding depends on a
decoding map for the corresponding code. As decoding maps are usually not
complete, embedding can fail. We propose a method to ensure or increase the
probability of embedding success for these stegoschemes. This method is based
on puncturing codes. We show how the use of punctured codes may also increase
the embedding efficiency of the obtained stegoschemes
Blindspot: Indistinguishable Anonymous Communications
Communication anonymity is a key requirement for individuals under targeted
surveillance. Practical anonymous communications also require
indistinguishability - an adversary should be unable to distinguish between
anonymised and non-anonymised traffic for a given user. We propose Blindspot, a
design for high-latency anonymous communications that offers
indistinguishability and unobservability under a (qualified) global active
adversary. Blindspot creates anonymous routes between sender-receiver pairs by
subliminally encoding messages within the pre-existing communication behaviour
of users within a social network. Specifically, the organic image sharing
behaviour of users. Thus channel bandwidth depends on the intensity of image
sharing behaviour of users along a route. A major challenge we successfully
overcome is that routing must be accomplished in the face of significant
restrictions - channel bandwidth is stochastic. We show that conventional
social network routing strategies do not work. To solve this problem, we
propose a novel routing algorithm. We evaluate Blindspot using a real-world
dataset. We find that it delivers reasonable results for applications requiring
low-volume unobservable communication.Comment: 13 Page
Paperless Transfer of Medical Images: Storing Patient Data in Medical Images
Medical images have become an integral part ofpatient diagnosis in recent years. With the introduction of HealthInformation Management Systems (HIMS) used for the storageand sharing of patient data, as well as the use of the PictureArchiving and Communication Systems (PACS) formanipulating and storage of CT Scans, X-rays, MRIs and othermedical images, the security of patient data has become a seriousconcern for medical professionals. The secure transfer of theseimages along with patient data is necessary for maintainingconfidentiality as required by the Data Protection Act, 2011 inTrinidad and Tobago and similar legislation worldwide. Tofacilitate this secure transfer, different digital watermarking andsteganography techniques have been proposed to safely hideinformation in these digital images. This paper focuses on theamount of data that can be embedded into typical medical imageswithout compromising visual quality. In addition, ExploitingModification Direction (EMD) is selected as the method of choicefor hiding information in medical images and it is compared tothe commonly used Least Significant Bit (LSB) method.Preliminary results show that by using EMD there little to nodistortion even at the highest embedding capacity
Errorless Robust JPEG Steganography using Outputs of JPEG Coders
Robust steganography is a technique of hiding secret messages in images so
that the message can be recovered after additional image processing. One of the
most popular processing operations is JPEG recompression. Unfortunately, most
of today's steganographic methods addressing this issue only provide a
probabilistic guarantee of recovering the secret and are consequently not
errorless. That is unacceptable since even a single unexpected change can make
the whole message unreadable if it is encrypted. We propose to create a robust
set of DCT coefficients by inspecting their behavior during recompression,
which requires access to the targeted JPEG compressor. This is done by dividing
the DCT coefficients into 64 non-overlapping lattices because one embedding
change can potentially affect many other coefficients from the same DCT block
during recompression. The robustness is then combined with standard
steganographic costs creating a lattice embedding scheme robust against JPEG
recompression. Through experiments, we show that the size of the robust set and
the scheme's security depends on the ordering of lattices during embedding. We
verify the validity of the proposed method with three typical JPEG compressors
and benchmark its security for various embedding payloads, three different ways
of ordering the lattices, and a range of Quality Factors. Finally, this method
is errorless by construction, meaning the embedded message will always be
readable.Comment: 10 pages, 11 figures, 1 table, submitted to IEEE Transactions on
Dependable and Secure Computin
Adaptive spatial image steganography and steganalysis using perceptual modelling and machine learning
Image steganography is a method for communicating secret messages under the cover images. A sender will embed the secret messages into the cover images according to an algorithm, and then the resulting image will be sent to the receiver. The receiver can extract the secret messages with the predefined algorithm. To counter this kind of technique, image steganalysis is proposed to detect the presence of secret messages.
After many years of development, current image steganography uses the adaptive algorithm for embedding the secrets, which automatically finds the complex area in the cover source to avoid being noticed. Meanwhile, image steganalysis has also been advanced to universal steganalysis, which does not require the knowledge of the steganographic algorithm. With the development of the computational hardware, i.e., Graphical Processing Units (GPUs), some computational expensive techniques are now available, i.e., Convolutional Neural Networks (CNNs), which bring a large improvement in the detection tasks in image steganalysis. To defend against the attacks, new techniques are also being developed to improve the security of image steganography, these include designing more scientific cost functions, the key in adaptive steganography, and generating stego images from the knowledge of the CNNs.
Several contributions are made for both image steganography and steganalysis in this thesis. Firstly, inspired by the Ranking Priority Profile (RPP), a new cost function for adaptive image steganography is proposed, which uses the two-dimensional Singular Spectrum Analysis (2D-SSA) and Weighted Median Filter (WMF) in the design. The RPP mainly includes three rules, i.e., the Complexity-First rule, the Clustering rule and the Spreading rule, to design a cost function. The 2D-SSA is employed in selecting the key components and clustering the embedding positions, which follows the Complexity-First rule and the Clustering rule. Also, the Spreading rule is followed to smooth the resulting image produced by 2D-SSA with WMF. The proposed algorithm has improved performance over four benchmarking approaches against non-shared selection channel attacks. It also provides comparable performance in selection-channel-aware scenarios, where the best results are observed when the relative payload is 0.3 bpp or larger. The approach is much faster than other model-based methods.
Secondly, for image steganalysis, to tackle more complex datasets that are close to the real scenarios and to push image steganalysis further to real-life applications, an Enhanced Residual Network with self-attention ability, i.e., ERANet, is proposed. By employing a more mathematically sophisticated way to extract more effective features in the images and the global self-Attention technique, the ERANet can further capture the stego signal in the deeper layers, hence it is suitable for the more complex situations in the new datasets. The proposed Enhanced Low-Level Feature Representation Module can be easily mounted on other CNNs in selecting the most representative features. Although it comes with a slightly extra computational cost, comprehensive experiments on the BOSSbase and ALASKA#2 datasets have demonstrated the effectiveness of the proposed methodology.
Lastly, for image steganography, with the knowledge from the CNNs, a novel postcost-optimization algorithm is proposed. Without modifying the original stego image and the original cost function of the steganography, and no need for training a Generative Adversarial Network (GAN), the proposed method mainly uses the gradient maps from a well-trained CNN to represent the cost, where the original cost map of the steganography is adopted to indicate the embedding positions. This method will smooth the gradient maps before adjusting the cost, which solves the boundary problem of the CNNs having multiple subnets. Extensive experiments have been carried out to validate the effectiveness of the proposed method, which provides state-of-the-art performance. In addition, compared to existing work, the proposed method is effcient in computing time as well.
In short, this thesis has made three major contributions to image steganography and steganalysis by using perceptual modelling and machine learning. A novel cost function and a post-cost-optimization function have been proposed for adaptive spatial image steganography, which helps protect the secret messages. For image steganalysis, a new CNN architecture has also been proposed, which utilizes multiple techniques for providing state of-the-art performance. Future directions are also discussed for indicating potential research.Image steganography is a method for communicating secret messages under the cover images. A sender will embed the secret messages into the cover images according to an algorithm, and then the resulting image will be sent to the receiver. The receiver can extract the secret messages with the predefined algorithm. To counter this kind of technique, image steganalysis is proposed to detect the presence of secret messages.
After many years of development, current image steganography uses the adaptive algorithm for embedding the secrets, which automatically finds the complex area in the cover source to avoid being noticed. Meanwhile, image steganalysis has also been advanced to universal steganalysis, which does not require the knowledge of the steganographic algorithm. With the development of the computational hardware, i.e., Graphical Processing Units (GPUs), some computational expensive techniques are now available, i.e., Convolutional Neural Networks (CNNs), which bring a large improvement in the detection tasks in image steganalysis. To defend against the attacks, new techniques are also being developed to improve the security of image steganography, these include designing more scientific cost functions, the key in adaptive steganography, and generating stego images from the knowledge of the CNNs.
Several contributions are made for both image steganography and steganalysis in this thesis. Firstly, inspired by the Ranking Priority Profile (RPP), a new cost function for adaptive image steganography is proposed, which uses the two-dimensional Singular Spectrum Analysis (2D-SSA) and Weighted Median Filter (WMF) in the design. The RPP mainly includes three rules, i.e., the Complexity-First rule, the Clustering rule and the Spreading rule, to design a cost function. The 2D-SSA is employed in selecting the key components and clustering the embedding positions, which follows the Complexity-First rule and the Clustering rule. Also, the Spreading rule is followed to smooth the resulting image produced by 2D-SSA with WMF. The proposed algorithm has improved performance over four benchmarking approaches against non-shared selection channel attacks. It also provides comparable performance in selection-channel-aware scenarios, where the best results are observed when the relative payload is 0.3 bpp or larger. The approach is much faster than other model-based methods.
Secondly, for image steganalysis, to tackle more complex datasets that are close to the real scenarios and to push image steganalysis further to real-life applications, an Enhanced Residual Network with self-attention ability, i.e., ERANet, is proposed. By employing a more mathematically sophisticated way to extract more effective features in the images and the global self-Attention technique, the ERANet can further capture the stego signal in the deeper layers, hence it is suitable for the more complex situations in the new datasets. The proposed Enhanced Low-Level Feature Representation Module can be easily mounted on other CNNs in selecting the most representative features. Although it comes with a slightly extra computational cost, comprehensive experiments on the BOSSbase and ALASKA#2 datasets have demonstrated the effectiveness of the proposed methodology.
Lastly, for image steganography, with the knowledge from the CNNs, a novel postcost-optimization algorithm is proposed. Without modifying the original stego image and the original cost function of the steganography, and no need for training a Generative Adversarial Network (GAN), the proposed method mainly uses the gradient maps from a well-trained CNN to represent the cost, where the original cost map of the steganography is adopted to indicate the embedding positions. This method will smooth the gradient maps before adjusting the cost, which solves the boundary problem of the CNNs having multiple subnets. Extensive experiments have been carried out to validate the effectiveness of the proposed method, which provides state-of-the-art performance. In addition, compared to existing work, the proposed method is effcient in computing time as well.
In short, this thesis has made three major contributions to image steganography and steganalysis by using perceptual modelling and machine learning. A novel cost function and a post-cost-optimization function have been proposed for adaptive spatial image steganography, which helps protect the secret messages. For image steganalysis, a new CNN architecture has also been proposed, which utilizes multiple techniques for providing state of-the-art performance. Future directions are also discussed for indicating potential research
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