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

Automatic image enhancement is increasingly becoming a popular tool for the smartphone environment The tool automatically enhances the image right after it has been stored to enhance user's experience But, because enhancements are subjective and dependent on the image, the original image is backed up to provide a recovery option This requirement inevitably increases the storage requirement In order to reduce it, a novel automatic contrast enhancement based on reversible data hiding (ACERDH) is proposed The proposed method mimics the equalization effect observed in the basic contrast enhancement technique called global histogram equalization, while providing reversibility The experiment visually show improved contrast Additional experiment was done to compare the embedding capacity with an another reversible data hiding based contrast enhancement technique [4] The proposed method is fit for automation, while providing data hiding capability and removing the additional storage requirement

/pdf/automatic-contrast-enhancement-using-reversible-data-hiding-3zqvfmddvo.pdf

Automatic contrast enhancement using reversible data hiding

Reversible watermarking is a kind of digital watermarking which is able to recover the original image exactly as well as extracting hidden message. Many algorithms have aimed at lower image distortion in higher embedding capacity. In the reversible data hiding, the role of efficient predictors is crucial. Recently, adaptive predictors using least square approach have been proposed to overcome the limitation of the fixed predictors. This paper proposes a novel reversible data hiding algorithm using least square predictor via least absolute shrinkage and selection operator (LASSO). This predictor is dynamic in nature rather than fixed. Experimental results show that the proposed method outperforms the previous methods including some algorithms which are based on the least square predictors.

https://link.springer.com/content/pdf/10.1186%2Fs13640-016-0144-3.pdf

Reversible data hiding using least square predictor via the LASSO

In this paper, we propose a risk-sensitive hinge loss function-based cognitive ensemble of extreme learning machine (ELM) classifiers for JPEG steganalysis. ELM is a single hidden-layer feed-forward network that chooses the input parameters randomly and estimates the output weights analytically. For steganalysis, we have extracted 548-dimensional merge features and trained ELM to approximate the functional relationship between the merge features and class label. Further, we use a cognitive ensemble of ELM classifier with risk-sensitive hinge loss function for accurate steganalysis. As the hinge loss error function is shown to be better than mean-squared error function for classification problems, here, the individual ELM classifiers are developed based on hinge loss error function. The cognition in the ensemble of ELM obtains the weighted sum of individual classifiers by enhancing the outputs of winning classifiers for a sample, while penalizing the other classifiers for the sample. Thus, the cognitive ensemble ELM classifier positively exploits the effect of initialization in each classifier to obtain the best results. The performance of the cognitive ensemble ELM in performing the steganalysis is compared to that of a single ELM, and the existing ensemble support vector machine classifier for steganalysis. Performance results show the superior classification ability of the cognitive ensemble ELM classifier.

A Cognitive Ensemble of Extreme Learning Machines for Steganalysis Based on Risk-Sensitive Hinge Loss Function

An O (1) time optimal algorithm for multiplying matrices on reconfigurable mesh

In this paper we present a simple and efficient method for detecting the blurriness in the pictures. Recently, many digital cameras are equipped with auto-focusing functions to help the users take well-focused pictures. However, digital images can be degraded by limited contrast, inappropriate exposure, imperfection of auto-focusing or motion compensating devices, limited knowledge of amateur photographers, and so on. In order to detect blurry images for deleting them or making them go through an enhancement process automatically, a reliable measure of image degradation is needed. This paper presents a blurriness/sharpness detection algorithm based on the prediction-error variance, and demonstrates its feasibility by using extensive experiments. This method is fast, easy to implement and accurate. Regardless of the detection accuracy, the proposed method in this paper is not demanding on computation time.

Hyoung Joong Kim

Papers

Automatic contrast enhancement using reversible data hiding

Reversible data hiding using least square predictor via the LASSO

A Cognitive Ensemble of Extreme Learning Machines for Steganalysis Based on Risk-Sensitive Hinge Loss Function

An O (1) time optimal algorithm for multiplying matrices on reconfigurable mesh

Efficient Method of Detecting Globally Blurry or Sharp Images