Acta Cybernetica

An Information Theoretic Image Steganalysis for LSB Steganography

2020-08-19T09:14:06+02:00

Steganography hides the data within a media file in an imperceptible way. Steganalysis exposes steganography by using detection measures. Traditionally, Steganalysis revealed steganography by targeting perceptible and statistical properties which results in developing secure steganography schemes. In this work, we target LSB image steganography by using entropy and joint entropy metrics for steganalysis. First, the Embedded image is processed for feature extraction then analyzed by entropy and joint entropy with their corresponding original image. Second, SVM and Ensemble classifiers are trained according to the analysis results. The decision of classifiers discriminates cover image from stego image. This scheme is further applied on attacked stego image for checking detection reliability. Performance evaluation of proposed scheme is conducted over grayscale image datasets. We analyzed LSB embedded images by Comparing information gain from entropy and joint entropy metrics. Results conclude that entropy of the suspected image is more preserving than joint entropy. As before histogram attack, detection rate with entropy metric is 70% and 98% with joint entropy metric. However after an attack, entropy metric ends with 30% detection rate while joint entropy metric gives 93% detection rate. Therefore, joint entropy proves to be better steganalysis measure with 93% detection accuracy and less false alarms with varying hiding ratio.

On the Steps of Emil Post: from Normal Systems to the Correspondence Decision Problem

2020-08-19T09:14:06+02:00

In 1946, Emil Leon Post (Bulletin of Amer. Math. Soc. 52 (1946), 264-268) introduced his famouscorrespondence decision problem, nowadays known as the Post Correspondence Problem (PCP).Post proved the undecidability of the PCP by areduction from his normal systems. In the presentarticle we follow the steps of Post, and give another, somewhat simpler and more straightforwardproof of the undecidability of the problem by using the same source of reductions as Post did.We investigate these, very different, techniques, and point out out some peculiarities in theapproach used by Post.

Estimating the Dimension Of The Subfield Subcodes of Hermitian Codes

2020-08-19T09:14:06+02:00

In this paper, we study the behavior of the true dimension of the subfield subcodes of Hermitian codes. Our motivation is to use these classes of linear codes to improve the parameters of the McEliece cryptosystem, such
as key size and security level. The McEliece scheme is one of the promising alternative cryptographic schemes to the current public key schemes since in the last four decades, they resisted all known quantum computing attacks. By computing and analyzing a data collection of true dimensions of subfield subcodes, we concluded that they can be estimated by the extreme value distribution function.

Graph Coloring based Heuristic for Crew Rostering

2020-08-19T09:11:58+02:00

In the last years personnel cost became a huge factor in the financial management of many companies and institutions.The firms are obligated to employ their workers in accordance with the law prescribing labour rules. The companies can save costs with minimizing the differences between the real and the expected worktimes. Crew rostering is assigning the workers to the previously determined shifts, which has been widely studied in the literature. In this paper, a mathematical model of the problem is presented and a two-phase graph coloring method for the crew rostering problem is introduced. Our method has been tested on artificially generated and real life input data. The results of the new algorithm have been compared to the solutions of the integer programming model for moderate-sized problems instances.

Type Inference of Simple Recursive Functions in Scala

2020-10-22T09:37:03+02:00

Scala is a well-established multi-paradigm programming language known for its
terseness that includes advanced type inference features. Unfortunately this
type inferring algorithm does not support typing of recursive functions.
This is both against the original design philosophies of Scala and puts an
unnecessary burden on the programmer. In this paper we propose a method to
compute the return types for simple recursive functions in Scala. We make
a heuristic assumption on the return type based on the non-recursive execution
branches and provide a proof of this method's correctness. The algorithm does not have
a significant effect on the compilation speed. We implemented our method as
an extension prototype in the Scala compiler and used it
to successfully test our method on various examples. The compiler extension
prototype is available for further tests.

Differential Testing of Static Analyzers

2020-10-22T09:38:16+02:00

Program faults, best known as bugs, are practically unavoidable in today's ever growing software systems. One increasingly popular way of eliminating them, besides tests, dynamic analysis, and fuzzing, is using static analysis based bug-finding tools. Such tools are capable of finding surprisingly sophisticated bugs automatically by inspecting the source code. Their analysis is usually both unsound and incomplete, but still very useful in practice, as they can find non-trivial problems in a reasonable time (e.g. within hours, for an industrial project) without human intervention.

Because the problems that static analyzers try to solve are hard, usually intractable, they use various approximations that need to be fine-tuned in order to grant a good user experience (i.e. as many interesting bugs with as few distracting false alarms as possible). For each newly introduced heuristic, this normally happens by performing differential testing of the analyzer on a lot of widely used open source software projects that are known to use related language constructs extensively. In practice, this process is ad hoc, error-prone, poorly reproducible and its results are hard to share.

We present a set of tools that aim to support the work of static analyzer developers by making differential testing easier. Our framework includes tools for automatic test suite selection, automated differential experiments, coverage information of increased granularity, statistics collection, metric calculations, and visualizations, all resulting in a convenient, shareable HTML report.

Improved Loop Execution Modeling in the Clang Static Analyzer

2020-10-22T09:39:23+02:00

The LLVM Clang Static Analyzer is a source code analysis tool which aims to find bugs in C, C++, and Objective-C programs using symbolic execution, i.e. it simulates the possible execution paths of the code. Currently the simulation of the loops is somewhat naive (but efficient), unrolling the loops a predefined constant number of times. However, this approach can result in a loss of coverage in various cases. This study aims to introduce two alternative approaches which can extend the current method and can be applied simultaneously: (1) determining loops worth to fully unroll with applied heuristics, and (2) using a widening mechanism to simulate an arbitrary number of iteration steps. These methods were evaluated on numerous open source projects, and proved to increase coverage in most of the cases. This work also laid the infrastructure for future loop modeling improvements.

Interval-Based Simulation of Zélus IVPs using DynIbex

2020-08-19T09:31:48+02:00

Modeling continuous-time dynamical systems is a complex task. Fortunately some dedicated programming languages exist to ease this work. Zélus is one such language that generates a simulation executable which can be used to study the behavior of the modeled system. However, such simulations cannot handle uncertainties on some parameters of the system. This makes it necessary to run multiple simulations to check that the system fulfills particular requirements (safety for instance) for all the values in the uncertainty ranges. Interval-based guaranteed integration methods provide a solution to this problem. The DynIbex library provides such methods but it requires a manual encoding of the system in a general purpose programming language (C++). This article presents an extension of the Zélus compiler to generate interval-based guaranteed simulations of IVPs using DynIbex. This extension is conservative since it does not break the existing compilation workflow.

Toward the Development of Iteration Procedures for the Interval-Based Simulation of Fractional-Order Systems

2020-10-22T09:40:43+02:00

In many fields of engineering as well as computational physics, it is necessary
to describe dynamic phenomena which are characterized by an infinitely long horizon of past state values. This infinite horizon of past data then influences the evolution of future state trajectories. Such phenomena can be characterized effectively by means of fractional-order differential equations. In contrast to classical <em>linear</em> ordinary differential equations, <em>linear</em> fractional-order models have frequency domain characteristics with amplitude responses that deviate from the classical integer multiples of ±20 dB per frequency decade and, respectively, deviate from integer multiples of ±π/2 in the limit values of their corresponding phase response. Although numerous simulation approaches have been developed in recent years for the numerical evaluation of fractional-order models with point-valued initial conditions and parameters, the robustness analysis of such system representations is still a widely open area of research. This statement is especially true if interval uncertainty is considered with respect to initial states and parameters. Therefore, this paper summarizes the current state-of-the-art concerning the simulation-based analysis of fractional-order dynamics with a restriction to those approaches that can be extended to set-valued (interval) evaluations for models with bounded uncertainty. Especially, it is shown how verified simulation techniques for integer-order models with uncertain parameters can be extended toward fractional counterparts. Selected linear as well as nonlinear illustrating examples conclude this paper to visualize algorithmic properties of the suggested interval-based simulation methodology and point out directions of ongoing research.