Digital watermarking is a key ingredient to copyright protection. It provides a solution to illegal copying of digital material and has many other useful applications such as broadcast monitoring and the recording of electronic transactions. Now, for the first time, there is a book that focuses exclusively on this exciting technology. Digital Watermarking covers the crucial research findings in the field: it explains the principles underlying digital watermarking technologies, describes the requirements that have given rise to them, and discusses the diverse ends to which these technologies are being applied. As a result, additional groundwork is laid for future developments in this field, helping the reader understand and anticipate new approaches and applications.

Digital Watermarking

Trends in extreme learning machines

Currently, the most successful approach to steganography in empirical objects, such as digital media, is to embed the payload while minimizing a suitably defined distortion function. The design of the distortion is essentially the only task left to the steganographer since efficient practical codes exist that embed near the payload-distortion bound. The practitioner’s goal is to design the distortion to obtain a scheme with a high empirical statistical detectability. In this paper, we propose a universal distortion design called universal wavelet relative distortion (UNIWARD) that can be applied for embedding in an arbitrary domain. The embedding distortion is computed as a sum of relative changes of coefficients in a directional filter bank decomposition of the cover image. The directionality forces the embedding changes to such parts of the cover object that are difficult to model in multiple directions, such as textures or noisy regions, while avoiding smooth regions or clean edges. We demonstrate experimentally using rich models as well as targeted attacks that steganographic methods built using UNIWARD match or outperform the current state of the art in the spatial domain, JPEG domain, and side-informed JPEG domain.

/pdf/universal-distortion-function-for-steganography-in-an-3uayce1tdz.pdf

Universal distortion function for steganography in an arbitrary domain

This paper presents a complete methodology for designing practical and highly-undetectable stegosystems for real digital media. The main design principle is to minimize a suitably-defined distortion by means of efficient coding algorithm. The distortion is defined as a weighted difference of extended state-of-the-art feature vectors already used in steganalysis. This allows us to "preserve" the model used by steganalyst and thus be undetectable even for large payloads. This framework can be efficiently implemented even when the dimensionality of the feature set used by the embedder is larger than 107. The high dimensional model is necessary to avoid known security weaknesses. Although high-dimensional models might be problem in steganalysis, we explain, why they are acceptable in steganography. As an example, we introduce HUGO, a new embedding algorithm for spatial-domain digital images and we contrast its performance with LSB matching. On the BOWS2 image database and in contrast with LSB matching, HUGO allows the embedder to hide 7× longer message with the same level of security level.

/pdf/using-high-dimensional-image-models-to-perform-highly-h4pbo3rq9n.pdf

Using high-dimensional image models to perform highly undetectable steganography

This paper presents a reversible or lossless watermarking algorithm for images without using a location map in most cases. This algorithm employs prediction errors to embed data into an image. A sorting technique is used to record the prediction errors based on magnitude of its local variance. Using sorted prediction errors and, if needed, though rarely, a reduced size location map allows us to embed more data into the image with less distortion. The performance of the proposed reversible watermarking scheme is evaluated using different images and compared with four methods: those of Kamstra and Heijmans, Thodi and Rodriguez, and Lee et al. The results clearly indicate that the proposed scheme can embed more data with less distortion.

Reversible Watermarking Algorithm Using Sorting and Prediction

A novel graphic user interface concept for car navigation is presented. Main concepts include the map orientation according to the direction of driving, and level of details to prune off useless information from the map. Virtual reality modeling language supports the implementation of these features.

A Novel Gui For Car Navigation System Based On Vrml

CAPTCHA(Completely Automated Public Turing test to tell Compute rs and Humans Apart) is a test used in computing to distinguish whether or not the user is computer or human. Many web sites mostly use the character-based CAPTCHA consisting of digits and characters. Recently, with the development of OCR technology, simple character-based CAPTCHA are broken quite easily. As an alternative, many web sites add noise to make it harder for recognition. In this paper, we analyzed the most rec ent CAPTCHA, which incorporates the addition of the natural images to obfuscate the characters. We proposed an efficient method using support vector machine to separate the characters from th e background image and use convolutional neural network to recognize each characters. As a result, 368 out of 1000 CAPTCHA s were correctly identified, it was demonstrated that the current CAPTCHA is not safe.Keywords: CAPTCHA, Breaking CAPTCHA, SVM, CNN, HSV color space * 접수일(2015년 5월 4일), 수정일(1차: 2015년 7월 14일,2차: 2015년 8월 3일), 게재확정일(2015년 8월 19일)*This work was supported by the National Research Foundation of Korea Grant funded by the Korean government(MEST) (NRF-2015R1A2 A2A01004587) and supported by Business for

http://koreascience.or.kr:80/article/JAKO201533678768374.pdf

Hyoung Joong Kim

Papers

A Novel Gui For Car Navigation System Based On Vrml

Breaking character and natural image based CAPTCHA using feature classification

Width Extraction of JPEG Fragment with a missing header

A Fast MRQI-Based Algorithm to Locate Minimum Eigenpairs

First Smart Contract Allowing Cryptoasset Recovery