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

The rapid uptake of mobile devices and the rising popularity of mobile applications and services pose unprecedented demands on mobile and wireless networking infrastructure. Upcoming 5G systems are evolving to support exploding mobile traffic volumes, real-time extraction of fine-grained analytics, and agile management of network resources, so as to maximize user experience. Fulfilling these tasks is challenging, as mobile environments are increasingly complex, heterogeneous, and evolving. One potential solution is to resort to advanced machine learning techniques, in order to help manage the rise in data volumes and algorithm-driven applications. The recent success of deep learning underpins new and powerful tools that tackle problems in this space. In this paper, we bridge the gap between deep learning and mobile and wireless networking research, by presenting a comprehensive survey of the crossovers between the two areas. We first briefly introduce essential background and state-of-the-art in deep learning techniques with potential applications to networking. We then discuss several techniques and platforms that facilitate the efficient deployment of deep learning onto mobile systems. Subsequently, we provide an encyclopedic review of mobile and wireless networking research based on deep learning, which we categorize by different domains. Drawing from our experience, we discuss how to tailor deep learning to mobile environments. We complete this survey by pinpointing current challenges and open future directions for research.

/pdf/deep-learning-in-mobile-and-wireless-networking-a-survey-ct8cy2kuqi.pdf

Deep Learning in Mobile and Wireless Networking: A Survey

The paper introduces a robust image watermark based on an invariant image feature vector. Normalized Zernike moments of an image are used as the vector. The watermark is generated by modifying the vector. The watermark signal is designed with Zernike moments. The signal is added to the cover image in the spatial domain after the reconstruction process. We extract the feature vector from the modified image and use it as the watermark. The watermark is detected by comparing the computed Zernike moments of the test image and the given watermark vector. Rotation invariance is achieved by taking the magnitude of the Zernike moments. An image normalization method is used for scale and translation invariance. The robustness of the proposed method is demonstrated and tested using Stirmark 3.1. The test results show that our watermark is robust with respect to geometrical distortions and compression.

Invariant image watermark using Zernike moments

As forgeries have become popular, the importance of forgery detection is much increased. Copy-move forgery, one of the most commonly used methods, copies a part of the image and pastes it into another part of the the image. In this paper, we propose a detection method of copy-move forgery that localizes duplicated regions using Zernike moments. Since the magnitude of Zernike moments is algebraically invariant against rotation, the proposed method can detect a forged region even though it is rotated. Our scheme is also resilient to the intentional distortions such as additive white Gaussian noise, JPEG compression, and blurring. Experimental results demonstrate that the proposed scheme is appropriate to identify the forged region by copy-rotate-move forgery.

Detection of copy-rotate-move forgery using Zernike moments

The rapid uptake of mobile devices and the rising popularity of mobile applications and services pose unprecedented demands on mobile and wireless networking infrastructure. Upcoming 5G systems are evolving to support exploding mobile traffic volumes, agile management of network resource to maximize user experience, and extraction of fine-grained real-time analytics. Fulfilling these tasks is challenging, as mobile environments are increasingly complex, heterogeneous, and evolving. One potential solution is to resort to advanced machine learning techniques to help managing the rise in data volumes and algorithm-driven applications. The recent success of deep learning underpins new and powerful tools that tackle problems in this space. 
In this paper we bridge the gap between deep learning and mobile and wireless networking research, by presenting a comprehensive survey of the crossovers between the two areas. We first briefly introduce essential background and state-of-the-art in deep learning techniques with potential applications to networking. We then discuss several techniques and platforms that facilitate the efficient deployment of deep learning onto mobile systems. Subsequently, we provide an encyclopedic review of mobile and wireless networking research based on deep learning, which we categorize by different domains. Drawing from our experience, we discuss how to tailor deep learning to mobile environments. We complete this survey by pinpointing current challenges and open future directions for research.

This paper addresses a novel robust watermarking method for digital images using local invariant features. Most previous water- marking algorithms are unable to resist geometric distortions that desyn- chronize the location where copyright information is inserted. We pro- pose a watermarking method that is robust to geometric distortions. In order to resist geometric distortions, we use a local invariant feature of the image called the scale-invariant feature transform SIFT, which is invariant to translation and scaling distortions. The watermark is inserted into the circular patches generated by the SIFT. Rotation invariance is achieved using the translation property of the polar-mapped circular patches. Our method belongs to the blind watermark, because we do not need the original image during detection. We have performed an inten- sive simulation to show the robustness of the proposed method. The simulation results support the contention that our method is robust against geometric distortion attacks as well as signal-processing attacks. We have compared our results with those of other methods, and our method outperforms them. © 2006 Society of Photo-Optical Instrumentation

Robust image watermarking using local invariant features

This paper presents an automated power model generation method for smart phone. Accurate power model can be achieved from usage pattern stored on the phone. This method can be applied to any mobile devices regardless of the manufacturer and model. The generated power model can be used to estimate energy consumption of the mobile device.

Smart phone power model generation using use pattern analysis

Image watermark that is resistant to geometric distortion is remained to be an unsolved problem. Difficulty of the probelm comes from the situation that the watermark should be extracted without any information of the original image. In this paper, we review this problem and propose a new watermarking scheme based on invariant pattern recognition theory. We propose an invariant watermark using the Radon transform and higher order spectra. A bispectrum feature vector of the image is used as the watermark. Our approach differs from the previous methods in that we embed watermark into the phase of the higher order spectra. Also, our Radon embedding grid outperforms the Fourier-Mellin based methods. We devised a new embedding method which allows detection of the watermark when there is no exact inverse function during embedding. As we use the Radon transform, our method can be used for medical images. We show the invariance of the designed watermark with mathematical proofs. Experimental results confirm that this scheme is resistant to geometric distortions.

/pdf/robust-image-watermark-using-radon-transform-and-bispectrum-4bj6vrd7p4.pdf

Robust Image Watermark Using Radon Transform and Bispectrum Invariants

In sensor networks, analyzing power consumption before actual deployment is crucial for maximizing service lifetime. This paper proposes an instruction-level power estimator (IPEN) for sensor networks. IPEN is an accurate and fine grain power estimation tool, using an instructionlevel simulator. It is independent of the operating system, so many different kinds of sensor node software can be simulated for estimation. We have developed the power model of a Micaz-compatible mote. The power consumption of the ATmega128L microcontroller is modeled with the base energy cost and the instruction overheads. The CC2420 communication component and other peripherals are modeled according to their operation states. The energy consumption estimation module profiles peripheral accesses and function calls while an application is running. IPEN has shown excellent power estimation accuracy, with less than 5% estimation error compared to real sensor network implementation. With IPEN’s high precision instruction-level energy prediction, users can accurately estimate a sensor network’s energy consumption and achieve fine-grained optimization of their software.

Hyungshin Kim

Papers

Invariant image watermark using Zernike moments

Robust image watermarking using local invariant features

Smart phone power model generation using use pattern analysis

Robust Image Watermark Using Radon Transform and Bispectrum Invariants

Instruction-Level Power Estimator for Sensor Networks