An Image-Based Information Hiding Approach with Chaotic Systems and Optimized Security

Due to advances in information technology, most of information can be kept electronically, consequently security of information of became a fundamental issues . Steganography is a technique of hiding information in digital media. A large number of commercial steganographic programs use the least significant bit embedding (LSB) as the method of choice for message hiding in 24-bit color images, 8-bit color images, and grayscale images. It is commonly believed that changes to the LSBs of colors cannot be detected due to noise that is always present in digital images. In this paper, it is proposed to use a LSB embedding algorithm to embed the payload bits of message image into cover image in spatial domain and detect LSB embedding in randomly scattered pixels in both 24-bit color images and 8bit grayscale or color images. By inspecting the differences in the number of regular and singular groups for the LSB and the "shifted LSB plane", it can reliably detect messages .By analyzing first order statistic like histogram analysis, entropy it can detect the presence of embedded data. The details of the system and results there of are presented in this paper. (4 pages)

The aim of the article is to analyze the branding of companies that produce and promote eco-products in the context of their "presence" in the digital space, and to formulate specific recommendations for improving branding practices in the digital space for these companies. Analyses results. The article is devoted to the study of the branding of eco-products in the digital space using the example of the activities of two companies ("Effa", "Yes Straws"), which are members of the UN Global Compact Network Ukraine, are oriented towards progress under Sustainable Development Goal 12 "Responsible consumption and production" and produce and sell eco-products. The specifics of these companies' presence in the digital space have been investigated, including their official websites, social media pages, and search results for major key queries through the Google search engine. Taking into account the results of the analysis of the presence of business in the digital space and other public information in digital media, a general vision of the positioning of eco-goods brands has been formed, the offers of which involve a high level of environmental responsibility throughout the entire life cycle of the goods, starting with the processes of organizing production and ending with the processes of disposal. Based on the analysis of branding features of the studied brands, the authors recommended specific practices that will contribute to the development of eco-goods branding in the digital space: 1) for the "Effa" brand: development of the company's website interface in Ukrainian; search engine optimization of the website by main keywords; technical enablement of targeted actions (by CTA, call-to-actions) through the company's website; placing customer reviews on the company's website; increasing activity on own pages in social networks; involvement of experts (dentists, ecologists, etc.) in creating content for social networks such as Facebook and Instagram; 2) for the "Yes Straws" brand: publishing information on the website about the product disposal process after its use; getting to know the team through various digital platforms (first of all, the website); enabling subscription to news through the website; publication of verified results comparing straws of «Yes Straws» with plastic straws; creating a page on LinkedIn; increasing activity on own pages in social networks; creation and placement of video content about the brand, eco-product, company on YouTube; optimization of the number of pages in Instagram. Conclusions and directions for further research. The list of recommendations for the development of eco-products branding in the digital space has been grouped. The methodical approach to brand evaluation of eco-products, including due to the peculiarities of the branding of these products in the digital space, has been determined as the priority direction for further research.

Assessing the level of digital health literacy among healthcare workers of teaching hospitals in the southeast of Iran

Steganography is a technique for hiding information in digital media so that it cannot be detected by third parties. One effective steganography method is Motion Vector Steganography (MVS), which utilizes motion vectors in video compression to insert secret messages. This study implements the Least Significant Bit (LSB) and Most Significant Bit (MSB) methods on motion vectors in videos to insert messages with minimal changes in visual quality. The results of the analysis show that the LSB method has little impact on video quality, while the MSB method is more resistant to steganalysis detection. However, there are challenges such as changes in file format and video size that need to be considered. This study provides insight into the application of video-based steganography and its potential development in the field of information security.

Whether voter sees message as issue or image material depends on predisposition This study argues that, like meaning, political issue and image information does not rest solely in the content of a political advertisement, but rests also in the minds of the perceivers. Although the mass media literature addresses the effects of cognitive variables on the processing of political information,1 this study explores the existence of a cognitive bias which affects an individual's processing of political advertisements. Research suggests that the selective recall of either image or issue political information may result from the motivation to attend to image or issue information,2 from the existence of a schema which facilitates the processing of image or issue information,3 or the elaboration of one's political belief system.4 But do these motivations, schemata and beliefs result in a predisposition to process predominantly image or issue information, regardless of the image and issue content of the information? This question is particularly relevant to political advertising research for two reasons: 1) political advertisers have long been criticized for focusing on candidates' images to the neglect of issue information,5 and 2) content analytic studies6 and studies of individual processing differences7 have not yet merged. Moreover, if processing biases exist, what are the demographic and political characteristics associated with image or issue information orientations? For example, Swanson and Freeman report that discriminating among political candidates on the basis of political criteria (e.g. issues, ideologies, and political background was not related to reported interest in politics, or the frequency with which people discuss politics with friends.8 However, it was related to political activities such as donating money to campaigns and working for campaigns. If we assume that political motivations, schemata and beliefs mediate the processing of political advertisements, two important research questions are raised. RQ 1: Do image and issue political information processing orientations exist? RQ 2: If image and issue processing biases exist, what political and demographic variables are related to this processing bias. Method The sample was composed of 113 members of men's and women's organizations (59% males, and 41% females). The groups were exposed to two televised political advertisements for opposing United States Senate candidates in another state. The ads were selected because of their relatively equal and similar image and issue information representation, the discussion of issues pertinent to the area, the unfamiliarity of the candidates to the subjects and the lack of partisan labels in the ads. Senatorial ads were employed because past research has shown that senatorial level campaigns initiate moderate to high political involvement among the general public.9 To further stimulate involvement in this artificial situation, subjects were led to believe that the candidates were seeking party nomination in their state for the next senatorial campaign. The ads were not systematically content analyzed for image and issue content. However, 25 pre-test subjects evaluated the ads for comparable ease in evaluating both candidates on all image and issue criteria. The specific differences between the ads is not central to the proposed hypothesis, however. The hypothesis argues that voters will respond consistently with an image or issue orientation regardless of the content of the ad. As a representation of initial information processing, cognitive responses seem a valid measure for identifying image and issue processing biases.10 The general paradigm for assessing cognitive responses in research involves oral or written listing of the subject's thoughts immediately following exposure to the message. Each of these responses were content analyzed as being image, issue or other. …

In the past decade, a large amount of important digital data has been created and stored in the form of color images; the protection of such data from undesirable accesses has become an important problem in information security. In this paper, a new approach based on an evolutionary framework is proposed for the secure encryption of color images. The image contents in a color image are first fully scrambled with a sequence of bit-level operations determined by a number of integer keys. A scrambled image is then encrypted with keys generated from an evolutionary process controlled by a set of chaotic systems. Analysis and experiments show that the proposed approach can generate encrypted color images with high security. In addition, the performance of the proposed approach is compared with that of a few state-of-the-art approaches for color image encryption. The results of the comparison suggest that the proposed approach outperforms the other approaches in the overall security of encrypted images. The proposed approach is thus potentially useful for applications that require color image encryption.

Color image triple zero-watermarking using decimal-order polar harmonic transforms and chaotic system

This chapter is devoted to the modification of an extension of dynamic programming approach for optimization of decision rules relative to length. “Classical” dynamic programming approach allows one to obtain optimal rules, i.e., rules with the minimum length. This fact is important from the point of view of knowledge representation. The idea of the dynamic programming approach for optimization of decision rules is based on a partitioning of a decision table into subtables. The algorithm constructs a directed acyclic graph. Basing on the constructed graph, sets of rules with the minimum length, attached to each row of a decision table, can be described. Proposed modification is based on the idea that not the complete graph is constructed but its part. It allows one to obtain values of length of decision rules close to optimal ones, and the size of the graph is smaller than in case of “classical” dynamic programming approach. The chapter also contains results of experiments with decision tables from UCI Machine Learning Repository.

A chaotic image encryption algorithm based on random dynamic mixing

Optimal Consumption Plans—A Dynamic Programming Approach

Using color histograms in automatic emotion recognition systems faces different issues. One of the important challenges is to determine the appropriate number of bins in the color histogram to achieve the highest recognition performance possible with minimal computations. This research focuses on emotion recognition induced by visual contents of images, or REVC for short, using ARTphoto dataset. Twenty-two different classifiers are used with color histograms in both RGB (red, green, blue) and HSV (hue, saturation, value) color spaces across different numbers of bins, and overall performance of each bin size is compared with that of other bin sizes. The research findings show that the performance of REVC system does not improve in terms of overall sensitivity rate, when the number of bins in color histogram is increased. Moreover, this paper identifies the advantage of using HSV color space over RGB in using color histogram for REVC systems. Furthermore, findings recognize the optimum number of bins in both RGB and HSV color spaces, and ANOVA (analysis of variance) is used to analyze experimental data, which identifies the optimum color histogram bin size used for HSV color space is significantly better than that used for RGB color space in REVC systems.

In this paper a new blind technique using one dimensional Walsh coding to improve the robustness of image watermarking is presented. The algorithm uses 1-D Walsh coding to embed mobile phone digits in color images captured by a still image camera. The new technique inserts the coded binary bits of the phone digits in the discrete cosine transform (DCT) blocks of the green channel of the digital color image. The use of the green channel produced better results compared to the previous DCT algorithms using the Y-Channel. Also, the Walsh coding has improved robustness against JPEG compression, cropping and some filtering attacks.

In modern production systems, it is critical to perform maintenance, calibration, installation, and upgrade tasks during planned downtime. Otherwise, the systems become unreliable and new product introductions are delayed. For reasons of safety, testing, and access, task performance often requires the vicinity of impacted equipment to be left in a specific “end state” when production halts. Therefore, planning the shutdown of a production system to balance production goals against enabling non-production tasks yields a challenging optimization problem. This article proposes a mathematical formulation of this problem and a dynamic programming approach that efficiently finds optimal shutdown policies for deterministic serial production lines. An event-triggered re-optimization procedure that is based on the proposed deterministic dynamic programming approach is also introduced for handling uncertainties in the production line for the stochastic case. It is demonstrated numerically that in these cases with random breakdowns and repairs, the re-optimization procedure is efficient and even obtains results that are optimal or nearly optimal.

Simple or proxy production models are potentially very useful and tractable because they are computationally attractive whilst still providing insight to the decision at hand. Useful and tractable models are required for supporting high-quality decisions in uncertain, complex, and computationally demanding contexts. A key decision for development planning is: what is the optimal time to initiate an Improved-Oil-Recovery (IOR) process. We aim to illustrate the implementation and application of a useful and tractable approach for the analysis of the optimal IOR switch time using a two-factor production model and Least-Squares Monte Carlo (LSM) simulation. The two-factor production model contains only two parameters for each recovery phases. One parameter describes how much recovery efficiency a recovery mechanism can ultimately achieve whilst the other describes how fast the recovery efficiency increases. The simplicity of the model makes it computationally attractive. The LSM algorithm is an approximate dynamic programming approach, which allows for learning over time. It provides a near-optimal solution for the IOR switch time problem. The Value-Of-Information (VOI) framework—a powerful decision-analysis tool—provides an estimate of the value of learning. Closed-Loop Reservoir Management (CLRM) is considered to be a state-of-the-art approach to solving for the optimal IOR switch time. However, this approach can produce a suboptimal solution as the CLRM approach considers only uncertainties and actions reflecting currently available information but not those uncertainties and actions arising from future information. The dynamic programming approach used here considers both the impact of the information obtained before a decision is made and the impact of the information that might be obtained to support future decisions. We conclude that a dynamic programming approach, such as the LSM algorithm, can significantly improve both the timing and value of decisions, leading to a significant increase in a field's economic performance. Furthermore, the two-factor model combined with the LSM algorithm is tractable and provides useful insight into the IOR switch time problem. The novelties provided by this work are: developing and illustrating the structure of the IOR switch time problem in a decision tree, demonstrating and discussing the suboptimality of the CLRM solution, developing and illustrating the detailed steps of applying the LSM algorithm for the IOR switch time decision, and implementing the two-factor model combined with the LSM algorithm for analyzing the optimal IOR switch time.

It is a challenging task to synthesize an embroidery image with complex texture from a colorful image. Existing style transfer methods to synthesize embroidery images will lead to color shift and texture clutter. In this paper, the generative adversarial network architecture with embroidery channel attention is proposed to synthesize embroidery images based on the unaligned dataset. Our method can synthesize the color and texture images generated separately from the features of the input image without extra data and cycle network. The generator with embroidery channel attention in our network can generate three attention masks (texture attention mask, color attention mask, original attention mask) and two content masks (color content mask and texture content mask). The color image and texture image of embroidery are synthesized separately with these masks. Meanwhile, a color loss function is proposed to encourage the color of the generated image to be close to that of the original image. In addition, a white padding processing technology is proposed to improve the stability of global embroidery texture synthesis. Existing extensive experiments show that our method synthesizes the embroidery images with realistic color and stable texture to solve the color shift and texture clutter. In the case of ensuring the content of the input images, the results synthesized by our method are closer to the real embroidery.