430 research outputs found
Reversible Data Hiding in Encrypted Images Using MSBs Integration and Histogram Modification
This paper presents a reversible data hiding in encrypted image that employs
based notions of the RDH in plain-image schemes including histogram
modification and prediction-error computation. In the proposed method, original
image may be encrypted by desire encryption algorithm. Most significant bit
(MSB) of encrypted pixels are integrated to vacate room for embedding data
bits. Integrated ones will be more resistant against failure of reconstruction
if they are modified for embedding data bits. At the recipient, we employ
chess-board predictor for lossless reconstruction of the original image by the
aim of prediction-error analysis. Comparing to existent RDHEI algorithms, not
only we propose a separable method to extract data bits, but also content-owner
may attain a perfect reconstruction of the original image without having data
hider key. Experimental results confirm that the proposed algorithm outperforms
state of the art ones
An Efficient MSB Prediction-Based Method for High-Capacity Reversible Data Hiding in Encrypted Images
International audienceReversible data hiding in encrypted images (RDHEI) is an effective technique to embed data in the encrypted domain. An original image is encrypted with a secret key and during or after its transmission, it is possible to embed additional information in the encrypted image, without knowing the encryp-tion key or the original content of the image. During the decoding process, the secret message can be extracted and the original image can be reconstructed. In the last few years, RDHEI has started to draw research interest. Indeed, with the development of cloud computing, data privacy has become a real issue. However, none of the existing methods allow us to hide a large amount of information in a reversible manner. In this paper, we propose a new reversible method based on MSB (most significant bit) prediction with a very high capacity. We present two approaches, these are: high capacity reversible data hiding approach with correction of prediction errors and high capacity reversible data hiding approach with embedded prediction errors. With this method, regardless of the approach used, our results are better than those obtained with current state of the art methods, both in terms of reconstructed image quality and embedding capacity
Pixel grouping of digital images for reversible data hiding
Pixel Grouping (PG) of digital images has been a key consideration in recent development of the Reversible Data Hiding (RDH) schemes. While a PG kernel with neighborhood pixels helps compute image groups for better embedding rate-distortion performance, only horizontal neighborhood pixel group of size 1×3 has so far been considered. In this paper, we formulate PG kernels of sizes 3×1, 2×3 and 3×2 and investigate their effect on the rate-distortion performance of a prominent PG-based RDH scheme. Specially, a kernel of size 3×2 (or 2×3) that creates a pair of pixel-trios having triangular shape and offers a greater possible correlation among the pixels. This kernel thus can be better utilized for improving a PG-based RDH scheme. Considering this, we develop and present an improved PG-based RDH scheme and the computational models of its key processes. Experimental results demonstrated that our proposed RDH scheme offers reasonably better embedding rate-distortion performance than the original scheme
Generalized PVO‐based dynamic block reversible data hiding for secure transmission using firefly algorithm
In this paper, we proposed a novel generalized pixel value ordering–based reversible data hiding using firefly algorithm (GPVOFA). The sequence of minimum and maximum number pixels value has been used to embed the secret data while prediction and modification are held on minimum, and the maximum number of pixel blocks is used to embed the secret data into multiple bits. The host image is divided into the size of noncoinciding dynamic blocks on the basis of firefly quadtree partition, whereas rough blocks are divided into a larger size; moreover, providing more embedding capacity used small flat blocks size and optimal location in the block to write the information. Our proposed method becomes able to embed large data into a host image with low distortion. The rich experimental results are better, as compared with related preceding arts
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
High capacity data embedding schemes for digital media
High capacity image data hiding methods and robust high capacity digital audio watermarking algorithms are studied in this thesis. The main results of this work are the development of novel algorithms with state-of-the-art performance, high capacity and transparency for image data hiding and robustness, high capacity and low distortion for audio watermarking.En esta tesis se estudian y proponen diversos métodos de data hiding de imágenes y watermarking de audio de alta capacidad. Los principales resultados de este trabajo consisten en la publicación de varios algoritmos novedosos con rendimiento a la altura de los mejores métodos del estado del arte, alta capacidad y transparencia, en el caso de data hiding de imágenes, y robustez, alta capacidad y baja distorsión para el watermarking de audio.En aquesta tesi s'estudien i es proposen diversos mètodes de data hiding d'imatges i watermarking d'àudio d'alta capacitat. Els resultats principals d'aquest treball consisteixen en la publicació de diversos algorismes nous amb rendiment a l'alçada dels millors mètodes de l'estat de l'art, alta capacitat i transparència, en el cas de data hiding d'imatges, i robustesa, alta capacitat i baixa distorsió per al watermarking d'àudio.Societat de la informació i el coneixemen
Audio Coding Based on Integer Transforms
Die Audiocodierung hat sich in den letzten Jahren zu einem sehr
populären Forschungs- und Anwendungsgebiet entwickelt. Insbesondere
gehörangepasste Verfahren zur Audiocodierung, wie etwa MPEG-1 Layer-3
(MP3) oder MPEG-2 Advanced Audio Coding (AAC), werden häufig zur
effizienten Speicherung und Übertragung von Audiosignalen verwendet. Für
professionelle Anwendungen, wie etwa die Archivierung und Übertragung im
Studiobereich, ist hingegen eher eine verlustlose Audiocodierung angebracht.
Die bisherigen Ansätze für gehörangepasste und verlustlose
Audiocodierung sind technisch völlig verschieden. Moderne
gehörangepasste Audiocoder basieren meist auf Filterbänken, wie etwa der
überlappenden orthogonalen Transformation "Modifizierte Diskrete
Cosinus-Transformation" (MDCT). Verlustlose Audiocoder hingegen
verwenden meist prädiktive Codierung zur Redundanzreduktion. Nur wenige
Ansätze zur transformationsbasierten verlustlosen Audiocodierung wurden
bisher versucht.
Diese Arbeit präsentiert einen neuen Ansatz hierzu, der das
Lifting-Schema auf die in der gehörangepassten Audiocodierung
verwendeten überlappenden Transformationen anwendet. Dies ermöglicht
eine invertierbare Integer-Approximation der ursprünglichen
Transformation, z.B. die IntMDCT als Integer-Approximation der MDCT. Die
selbe Technik kann auch für Filterbänke mit niedriger Systemverzögerung
angewandt werden. Weiterhin ermöglichen ein neuer, mehrdimensionaler
Lifting-Ansatz und eine Technik zur Spektralformung von
Quantisierungsfehlern eine Verbesserung der Approximation der
ursprünglichen Transformation.
Basierend auf diesen neuen Integer-Transformationen werden in dieser
Arbeit neue Verfahren zur Audiocodierung vorgestellt. Die Verfahren
umfassen verlustlose Audiocodierung, eine skalierbare verlustlose
Erweiterung eines gehörangepassten Audiocoders und einen integrierten
Ansatz zur fein skalierbaren gehörangepassten und verlustlosen
Audiocodierung. Schließlich wird mit Hilfe der Integer-Transformationen
ein neuer Ansatz zur unhörbaren Einbettung von Daten mit hohen
Datenraten in unkomprimierte Audiosignale vorgestellt.In recent years audio coding has become a very popular field for
research and applications. Especially perceptual audio coding schemes,
such as MPEG-1 Layer-3 (MP3) and MPEG-2 Advanced Audio Coding (AAC), are
widely used for efficient storage and transmission of music
signals. Nevertheless, for professional applications, such as archiving
and transmission in studio environments, lossless audio coding schemes
are considered more appropriate.
Traditionally, the technical approaches used in perceptual and lossless
audio coding have been separate worlds. In perceptual audio coding, the
use of filter banks, such as the lapped orthogonal transform "Modified
Discrete Cosine Transform" (MDCT), has been the approach of choice being
used by many state of the art coding schemes. On the other hand,
lossless audio coding schemes mostly employ predictive coding of
waveforms to remove redundancy. Only few attempts have been made so far
to use transform coding for the purpose of lossless audio coding.
This work presents a new approach of applying the lifting scheme to
lapped transforms used in perceptual audio coding. This allows for an
invertible integer-to-integer approximation of the original transform,
e.g. the IntMDCT as an integer approximation of the MDCT. The same
technique can also be applied to low-delay filter banks. A generalized,
multi-dimensional lifting approach and a noise-shaping technique are
introduced, allowing to further optimize the accuracy of the
approximation to the original transform.
Based on these new integer transforms, this work presents new audio
coding schemes and applications. The audio coding applications cover
lossless audio coding, scalable lossless enhancement of a perceptual
audio coder and fine-grain scalable perceptual and lossless audio
coding. Finally an approach to data hiding with high data rates in
uncompressed audio signals based on integer transforms is described
Audio Coding Based on Integer Transforms
Die Audiocodierung hat sich in den letzten Jahren zu einem sehr
populären Forschungs- und Anwendungsgebiet entwickelt. Insbesondere
gehörangepasste Verfahren zur Audiocodierung, wie etwa MPEG-1 Layer-3
(MP3) oder MPEG-2 Advanced Audio Coding (AAC), werden häufig zur
effizienten Speicherung und Übertragung von Audiosignalen verwendet. Für
professionelle Anwendungen, wie etwa die Archivierung und Übertragung im
Studiobereich, ist hingegen eher eine verlustlose Audiocodierung angebracht.
Die bisherigen Ansätze für gehörangepasste und verlustlose
Audiocodierung sind technisch völlig verschieden. Moderne
gehörangepasste Audiocoder basieren meist auf Filterbänken, wie etwa der
überlappenden orthogonalen Transformation "Modifizierte Diskrete
Cosinus-Transformation" (MDCT). Verlustlose Audiocoder hingegen
verwenden meist prädiktive Codierung zur Redundanzreduktion. Nur wenige
Ansätze zur transformationsbasierten verlustlosen Audiocodierung wurden
bisher versucht.
Diese Arbeit präsentiert einen neuen Ansatz hierzu, der das
Lifting-Schema auf die in der gehörangepassten Audiocodierung
verwendeten überlappenden Transformationen anwendet. Dies ermöglicht
eine invertierbare Integer-Approximation der ursprünglichen
Transformation, z.B. die IntMDCT als Integer-Approximation der MDCT. Die
selbe Technik kann auch für Filterbänke mit niedriger Systemverzögerung
angewandt werden. Weiterhin ermöglichen ein neuer, mehrdimensionaler
Lifting-Ansatz und eine Technik zur Spektralformung von
Quantisierungsfehlern eine Verbesserung der Approximation der
ursprünglichen Transformation.
Basierend auf diesen neuen Integer-Transformationen werden in dieser
Arbeit neue Verfahren zur Audiocodierung vorgestellt. Die Verfahren
umfassen verlustlose Audiocodierung, eine skalierbare verlustlose
Erweiterung eines gehörangepassten Audiocoders und einen integrierten
Ansatz zur fein skalierbaren gehörangepassten und verlustlosen
Audiocodierung. Schließlich wird mit Hilfe der Integer-Transformationen
ein neuer Ansatz zur unhörbaren Einbettung von Daten mit hohen
Datenraten in unkomprimierte Audiosignale vorgestellt.In recent years audio coding has become a very popular field for
research and applications. Especially perceptual audio coding schemes,
such as MPEG-1 Layer-3 (MP3) and MPEG-2 Advanced Audio Coding (AAC), are
widely used for efficient storage and transmission of music
signals. Nevertheless, for professional applications, such as archiving
and transmission in studio environments, lossless audio coding schemes
are considered more appropriate.
Traditionally, the technical approaches used in perceptual and lossless
audio coding have been separate worlds. In perceptual audio coding, the
use of filter banks, such as the lapped orthogonal transform "Modified
Discrete Cosine Transform" (MDCT), has been the approach of choice being
used by many state of the art coding schemes. On the other hand,
lossless audio coding schemes mostly employ predictive coding of
waveforms to remove redundancy. Only few attempts have been made so far
to use transform coding for the purpose of lossless audio coding.
This work presents a new approach of applying the lifting scheme to
lapped transforms used in perceptual audio coding. This allows for an
invertible integer-to-integer approximation of the original transform,
e.g. the IntMDCT as an integer approximation of the MDCT. The same
technique can also be applied to low-delay filter banks. A generalized,
multi-dimensional lifting approach and a noise-shaping technique are
introduced, allowing to further optimize the accuracy of the
approximation to the original transform.
Based on these new integer transforms, this work presents new audio
coding schemes and applications. The audio coding applications cover
lossless audio coding, scalable lossless enhancement of a perceptual
audio coder and fine-grain scalable perceptual and lossless audio
coding. Finally an approach to data hiding with high data rates in
uncompressed audio signals based on integer transforms is described
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