Each copy of a text document can be made different in a nearly invisible way by repositioning or modifying the appearance of different elements of text, i.e., lines, words, or characters. A unique copy can be registered with its recipient, so that subsequent unauthorized copies that are retrieved can be traced back to the original owner. In this paper we describe and compare several mechanisms for marking documents and several other mechanisms for decoding the marks after documents have been subjected to common types of distortion. The marks are intended to protect documents of limited value that are owned by individuals who would rather possess a legal than an illegal copy if they can be distinguished. We will describe attacks that remove the marks and countermeasures to those attacks. An architecture is described for distributing a large number of copies without burdening the publisher with creating and transmitting the unique documents. The architecture also allows the publisher to determine the identity of a recipient who has illegally redistributed the document, without compromising the privacy of individuals who are not operating illegally. Two experimental systems are described. One was used to distribute an issue of the IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, and the second was used to mark copies of company private memoranda.

Copyright protection for the electronic distribution of text documents : Identification and protection of multimedia information

In recent years, linguistic steganography based on text auto-generation technology has been greatly developed, which is considered to be a very promising but also a very challenging research topic. Previous works mainly focus on optimizing the language model and conditional probability coding methods, aiming at generating steganographic sentences with better quality. In this paper, we first report some of our latest experimental findings, which seem to indicate that the quality of the generated steganographic text cannot fully guarantee its steganographic security, and even has a prominent perceptual-imperceptibility and statistical-imperceptibility conflict effect (Psic Effect). To further improve the imperceptibility and security of generated steganographic texts, in this paper, we propose a new linguistic steganography based on Variational Auto-Encoder (VAE), which can be called VAE-Stega. We use the encoder in VAE-Stega to learn the overall statistical distribution characteristics of a large number of normal texts, and then use the decoder in VAE-Stega to generate steganographic sentences which conform to both of the statistical language model as well as the overall statistical distribution of normal sentences, so as to guarantee both the perceptual-imperceptibility and statistical-imperceptibility of the generated steganographic texts at the same time. We design several experiments to test the proposed method. Experimental results show that the proposed model can greatly improve the imperceptibility of the generated steganographic sentences and thus achieves the state of the art performance.

VAE-Stega: Linguistic Steganography Based on Variational Auto-Encoder

In image pre-processing, edge detection is a non-trivial task. Sometimes, images are affected by vagueness so that the edges of objects are difficult to distinguish. Hence, the usual edge-detecting operators can give unreliable results, thus necessitating the use of fuzzy procedures. In literature, Chaira and Ray approach is a popular technique for fuzzy edge detection in which fuzzy divergence formulation is exploited. However, this approach does not specify the threshold technique must be applied. Then, in this work, starting from Chairy and Ray procedure, we present a new fuzzy edge detector based on both fuzzy divergence (thought and proved to be a distance) and fuzzy entropy minimization for the thresholding sub-step in gray-scale images. Eddy currents, thermal infrared, and electrospinning images were used to test the proposed procedure after their fuzzification by a suitable adaptive S-shaped fuzzy membership function. Moreover, the fuzziness content of each image has been quantified by new specific indices proposed here and formulated in terms of fuzzy divergence. The results have been evaluated by suitable assessment metrics here formulated and are considered to be encouraging when qualitatively and quantitatively compared with those obtained by some well-known I- and II-order edge detectors.

Image Edge Detection: A New Approach Based on Fuzzy Entropy and Fuzzy Divergence

This research paper is an attempt to present Content Based Image Retrieval (CBIR) system developed for retrieving diseased leaves of soybean. It uses color, shape and texture features of leaf. Color features are extracted using HSV color histogram. Scale Invariant Feature Transform (SIFT) provides shape features in the form of matching key points. Local Binary Pattern (LBP) and Gabor filter are widely used texture features. Novel texture feature named Local Gray Gabor Pattern (LGGP) is proposed by combining LBP and Gabor. Performance of all these features with respect to retrieval precision is tested for three soybean leaf diseases. Further color, shape and texture features are combined to increase performance. It is found that when LGGP is combined with color histogram and SIFT retrieval precision is improved. Retrieval efficiency of about 96%, 68% and 76% is achieved for soybean leaves affected by mosaic virus, septoria brown spot and pod mottle disease respectively. Average retrieval efficiency of 80% (for the top 5 retrieval) and 72% (for the top 10 retrieval) is obtained by combined features. This retrieval precision is database dependent and varies with size of the database and quality of images.

Analysis of content based image retrieval for plant leaf diseases using color, shape and texture features

With the rapid development of natural language processing technologies, more and more text steganographic methods based on automatic text generation technology have appeared in recent years. These models use the powerful self-learning and feature extraction ability of the neural networks to learn the feature expression of massive normal texts. Then, they can automatically generate dense steganographic texts which conform to such statistical distribution based on the learned statistical patterns. In this letter, we observe that the conditional probability distribution of each word in the automatically generated steganographic texts will be distorted after embedded with hidden information. We use recurrent neural networks to extract these feature distribution differences and then classify those features into cover text and stego text categories. Experimental results show that the proposed model can achieve high detection accuracy. Besides, the proposed model can even make use of the subtle differences of the feature distribution of texts to estimate the amount of hidden information embedded in the generated steganographic text.

TS-RNN: Text Steganalysis Based on Recurrent Neural Networks

Radio-over-free-space optics (Ro-FSO) technology harnesses the large capacity of optical fiber and the mobility native to wireless networks. To increase the capacity of Ro-FSO networks, this work introduces a new optical mode division multiplexing (MDM) scheme for Ro-FSO by incorporating optical Hermite-Gaussian modes HG 0, 1 and HG 0, 3 for transmitting two distinct 2.5 Gbps data modulated on 10 GHz radio subcarriers. Signal-to-noise ratios and biterror-rates show successful 2 × 2.5Gbps data transmission of 10GHz radio-modulated subcarriers over the two HG optical carriers through a 800m free-space channel.

5Gbps HG 0,1 and HG 0,3 optical mode division multiplexing for RoFSO

This paper presents a review of the compression technique in digital image processing. As well as a brief description of the main technologies and traditional format that commonly used in image compression. It can be defined as image compression a set of techniques that are applied to the images to store or transfer them in an effective way. In addition, this paper presents formats that use to reduce redundant information in an image, unnecessary pixels and non-visual redundancy. The conclusion of this paper The results for this paper concludes that image compression is a critical issue in digital image processing because it allows us to store or transmit image data efficiently.

https://www.beei.org/index.php/EEI/article/download/2085/1354

Review on techniques and file formats of image compression

This paper presents several Public Key Cryptography (PKC) algorithms based on the perspective of researchers’ effort since it was invented in the last four decades. The categories of the algorithms had been analyzed which are Discrete Logarithm, Integer Factorization, Coding Theory, Elliptic Curve, Lattices, Digital Signature and Hybrid algorithms. This paper reviewed the previous schemes in different PKC algorithms. The aim of this paper is to present the comparative trends of PKC algorithms based on number of research for each algorithm in last four decades, the roadmap of PKC algorithms since they were invented and the most chosen algorithms among previous researchers. Finally, the strength and drawback of proposed schemes and algorithms also presented in this paper.

/pdf/analysis-review-on-public-key-cryptography-algorithms-392f9ox9y5.pdf

Analysis Review on Public Key Cryptography Algorithms

In this paper, we present a consolidated view of digital media steganalysis from the perspective of computational
intelligence.In our analysis the digital media steganalysis is divided into three domains which are image steganalysis, audio steganalysis, and video steganalysis.Three major computational intelligence methods have also been identified in the steganalysis domains which are bayesian, neural network, and genetic algorithm.Each of these methods has its own pros and cons.

/pdf/digital-steganalysis-computational-intelligence-approach-2a2ts53g7l.pdf

Digital steganalysis: Computational intelligence approach

Content-based image retrieval is an active research issue that had been famous from 1990s till presentThe main target of CBIR is to get accurate results with lower computational timeThis research report discusses on the comparative method used in color histogram based on three major methods used frequently in CBIR which are; normal color histogram using GLCM, color histogram using K-Means, and color histogram using Gabor filteringA set of 1000 images that cover 6 categories of images from bus, beach, building, flower, horse, and mountain are used to test the accuracy and the precision of each methodUsing Euclidean distance, similarity between queried image and the candidate images are calculatedExperiment results shows that color histogram with GLCM method had high accuracy and precise compared to K-Means and GaborFuture work will be made to add more features that are famous in CBIR which are texture, color, and shape features in order to get better results

Roshidi Din

Papers

5Gbps HG 0,1 and HG 0,3 optical mode division multiplexing for RoFSO

Review on techniques and file formats of image compression

Analysis Review on Public Key Cryptography Algorithms

Digital steganalysis: Computational intelligence approach

Comparative analysis of content-based image retrieval techniques