7,119 research outputs found
A Survey on Reversible Image Data Hiding Using the Hierarchical Block Embedding Technique
The use of graphics for data concealment has significantly advanced the fields of secure communication and identity verification. Reversible data hiding (RDH) involves hiding data within host media, such as images, while allowing for the recovery of the original cover. Various RDH approaches have been developed, including difference expansion, interpolation techniques, prediction, and histogram modification. However, these methods were primarily applied to plain photos. This study introduces a novel reversible image transformation technique called Block Hierarchical Substitution (BHS). BHS enhances the quality of encrypted images and enables lossless restoration of the secret image with a low Peak Signal-to-Noise Ratio (PSNR). The cover image is divided into non-overlapping blocks, and the pixel values within each block are encrypted using the modulo function. This ensures that the linear prediction difference in the block remains consistent before and after encryption, enabling independent data extraction without picture decryption. In order to address the challenges associated with secure multimedia data processing, such as data encryption during transmission and storage, this survey investigates the specific issues related to reversible data hiding in encrypted images (RDHEI). Our proposed solution aims to enhance security (low Mean Squared Error) and improve the PSNR value by applying the method to encrypted images
Prediction-error of Prediction Error (PPE)-based Reversible Data Hiding
This paper presents a novel reversible data hiding (RDH) algorithm for
gray-scaled images, in which the prediction-error of prediction error (PPE) of
a pixel is used to carry the secret data. In the proposed method, the pixels to
be embedded are firstly predicted with their neighboring pixels to obtain the
corresponding prediction errors (PEs). Then, by exploiting the PEs of the
neighboring pixels, the prediction of the PEs of the pixels can be determined.
And, a sorting technique based on the local complexity of a pixel is used to
collect the PPEs to generate an ordered PPE sequence so that, smaller PPEs will
be processed first for data embedding. By reversibly shifting the PPE histogram
(PPEH) with optimized parameters, the pixels corresponding to the altered PPEH
bins can be finally modified to carry the secret data. Experimental results
have implied that the proposed method can benefit from the prediction procedure
of the PEs, sorting technique as well as parameters selection, and therefore
outperform some state-of-the-art works in terms of payload-distortion
performance when applied to different images.Comment: There has no technical difference to previous versions, but rather
some minor word corrections. A 2-page summary of this paper was accepted by
ACM IH&MMSec'16 "Ongoing work session". My homepage: hzwu.github.i
The foundational legacy of ASL
Abstract. We recall the kernel algebraic specification language ASL and outline its main features in the context of the state of research on algebraic specification at the time it was conceived in the early 1980s. We discuss the most significant new ideas in ASL and the influence they had on subsequent developments in the field and on our own work in particular.
Interpolant-Based Transition Relation Approximation
In predicate abstraction, exact image computation is problematic, requiring
in the worst case an exponential number of calls to a decision procedure. For
this reason, software model checkers typically use a weak approximation of the
image. This can result in a failure to prove a property, even given an adequate
set of predicates. We present an interpolant-based method for strengthening the
abstract transition relation in case of such failures. This approach guarantees
convergence given an adequate set of predicates, without requiring an exact
image computation. We show empirically that the method converges more rapidly
than an earlier method based on counterexample analysis.Comment: Conference Version at CAV 2005. 17 Pages, 9 Figure
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