On the Implementation of Spread Spectrum Fingerprinting in Asymmetric Cryptographic Protocol

<p/> <p>Digital fingerprinting of multimedia contents involves the generation of a fingerprint, the embedding operation, and the realization of traceability from redistributed contents. Considering a buyer's right, the asymmetric property in the transaction between a buyer and a seller must be achieved using a cryptographic protocol. In the conventional schemes, the implementation of a watermarking algorithm into the cryptographic protocol is not deeply discussed. In this paper, we propose the method for implementing the spread spectrum watermarking technique in the fingerprinting protocol based on the homomorphic encryption scheme. We first develop a rounding operation which converts real values into integer and its compensation, and then explore the tradeoff between the robustness and communication overhead. Experimental results show that our system can simulate Cox's spread spectrum watermarking method into asymmetric fingerprinting protocol.</p

Spread Spectrum Watermarking: Principles and Applications in Fading Channel

ISBN 978-953-51-0618-

Collusion Resistive Framework for Multimedia Security

The recent advances in multimedia and Internet technology rises the need for multimedia security.The frequent distribution of multimedia content can cause security breach and violate copyright protection law.The legitimate user can come together to generate illegitimate copy to use it for unintended purpose.The most effective such kind of attack is collusion,involve group of user to contribute with their copies of content to generate a new copy. Fingerprinting,a unique mark is embedded have one to one corresponds with user,is the solution to tackle collusion attack problem.A colluder involve in collusion leaves its trace in alter copy,so the effectiveness of mounting a successful attack lies in how effectively a colluder alter the image by leaving minimum trace.A framework,step by step procedure to tackle collusion attack, involves fingerprint generation and embedding.Various fingerprint generation and embedding techniques are used to make collusion resistive framework effective.Spread spectrum embedding with coded modulation is most effective framework to tackle collusion attack problem.The spread spectrum framework shows high collusion resistant and traceability but it can be attacked with some special collusion attack like interleaving attack and combination of average attack.Various attacks have different post effect on multimedia in different domains. The thesis provide a detail analysis of various collusion attack in different domains which serve as basis for designing the framework to resist collusion.Various statistical and experimental resuslts are drwan to show the behavior of collusion attack.The thesis also proposed a framework here uses modified ECC coded fingerprint for generation and robust watermarking embedding using wave atom.The system shows high collusion resistance against various attack.Various experiments are are drawn and system shows high collusion resistance and much better performance than literature System

Preprint: Using RF-DNA Fingerprints To Classify OFDM Transmitters Under Rayleigh Fading Conditions

The Internet of Things (IoT) is a collection of Internet connected devices capable of interacting with the physical world and computer systems. It is estimated that the IoT will consist of approximately fifty billion devices by the year 2020. In addition to the sheer numbers, the need for IoT security is exacerbated by the fact that many of the edge devices employ weak to no encryption of the communication link. It has been estimated that almost 70% of IoT devices use no form of encryption. Previous research has suggested the use of Specific Emitter Identification (SEI), a physical layer technique, as a means of augmenting bit-level security mechanism such as encryption. The work presented here integrates a Nelder-Mead based approach for estimating the Rayleigh fading channel coefficients prior to the SEI approach known as RF-DNA fingerprinting. The performance of this estimator is assessed for degrading signal-to-noise ratio and compared with least square and minimum mean squared error channel estimators. Additionally, this work presents classification results using RF-DNA fingerprints that were extracted from received signals that have undergone Rayleigh fading channel correction using Minimum Mean Squared Error (MMSE) equalization. This work also performs radio discrimination using RF-DNA fingerprints generated from the normalized magnitude-squared and phase response of Gabor coefficients as well as two classifiers. Discrimination of four 802.11a Wi-Fi radios achieves an average percent correct classification of 90% or better for signal-to-noise ratios of 18 and 21 dB or greater using a Rayleigh fading channel comprised of two and five paths, respectively.Comment: 13 pages, 14 total figures/images, Currently under review by the IEEE Transactions on Information Forensics and Securit

Signal Processing in Wireless Communications: Device Fingerprinting and Wide-Band Interference Rejection

The rapid progress of wireless communication technologies that has taken place in recent years has significantly improved the quality of everyday life. However with this expansion of wireless communication systems come significant security threats and significant technological challenges, both of which are due to the fact that the communication medium is shared. The ubiquity of open wireless Internet access networks creates a new avenue for cyber-criminals to impersonate and act in an unauthorized way. The increasing number of deployed wide-band wireless communication systems entails technological challenges for effective utilization of the shared medium, which implies the need for advanced interference rejection methods. Wireless security and interference rejection in wide-band wireless communications are therefore often considered as the two main challenges in wireless network\u27s design and research. Important aspects of these challenges are illuminated and addressed in this dissertation. This dissertation considers signal processing approaches for exploiting or mitigating the effects of non-ideal components in wireless communication systems. In the first part of the dissertation, we introduce and study a novel, model-based approach to wireless device identification that exploits imperfections in the transmitter caused by manufacturing process nonidealities. Previous approaches to device identification based on hardware imperfections vary from transient analysis to machine learning but have not provided verifiable accuracy. Here, we detail a model-based approach, that uses statistical models of RF transmitter components: digital-to-analog converter, power amplifier and RF oscillator, which are amenable for analysis. Our proposed approach examines the key device characteristics that cause anonymity loss, countermeasures that can be applied by the nodes to regain the anonymity, and ways of thwarting such countermeasures. We develop identification algorithms based on statistical signal processing methods and address the challenging scenario when the units that need to be distinguished from one another are of the same model and from the same manufacturer. Using simulations and measurements of components that are commonly used in commercial communications systems, we show that our anonymity breaking techniques are effective. In the second part of the dissertation, we consider innovative approaches for the acquisition of frequency-sparse signals with wide-band receivers when a weak signal of interest is received in the presence of a very strong interference, and the effects of the nonlinearities in the low-noise amplifier at the receiver must be mitigated. All samples with amplitude above a given threshold, dictated by the linear input range of the receiver, are discarded to avoid the distortion caused by saturation of the low noise amplifier. Such a sampling scheme, while avoiding nonlinear distortion that cannot be corrected in the digital domain, poses challenges for signal reconstruction techniques, as the samples are taken non-uniformly, but also non-randomly. The considered approaches fall into the field of compressive sensing (CS); however, what differentiates them from conventional CS is that a structure is forced upon the measurement scheme. Such a structure causes a violation of the core CS assumption of the measurements\u27 randomness. We consider two different types of structured acquisition: signal independent and signal dependent structured acquisition. For the first case, we derive bounds on the number of samples needed for successful CS recovery when samples are drawn at random in predefined groups. For the second case, we consider enhancements of CS recovery methods when only small-amplitude samples of the signal that needs to be recovered are available for the recovery. Finally, we address a problem of spectral leakage due to the limited processing block size of block processing, wide-band receivers and propose an adaptive block size adjustment method, which leads to significant dynamic range improvements

The impact of Rayleigh fading channel effects on the RF-DNA fingerprinting process

The Internet of Things (IoT) consists of many electronic and electromechanical devices connected to the Internet. It is estimated that the number of connected IoT devices will be between 20 and 50 billion by the year 2020. The need for mechanisms to secure IoT networks will increase dramatically as 70% of the edge devices have no encryption. Previous research has proposed RF-DNA fingerprinting to provide wireless network access security through the exploitation of PHY layer features. RF-DNA fingerprinting takes advantage of unique and distinct characteristics that unintentionally occur within a given radio’s transmit chain during waveform generation. In this work, the application of RF-DNA fingerprinting is extended by developing a Nelder-Mead-based algorithm that estimates the coefficients of an indoor Rayleigh fading channel. The performance of the Nelder-Mead estimator is compared to the Least Square estimator and is assessed with degrading signal-to-noise ratio. The Rayleigh channel coefficients set estimated by the Nelder-Mead estimator is used to remove the multipath channel effects from the radio signal. The resulting channel-compensated signal is the region where the RF-DNA fingerprints are generated and classified. For a signal-to-noise ratio greater than 21 decibels, an average percent correct classification of more than 95% was achieved in a two-reflector channel

Digital audio watermarking for broadcast monitoring and content identification

Copyright legislation was prompted exactly 300 years ago by a desire to protect authors against exploitation of their work by others. With regard to modern content owners, Digital Rights Management (DRM) issues have become very important since the advent of the Internet. Piracy, or illegal copying, costs content owners billions of dollars every year. DRM is just one tool that can assist content owners in exercising their rights. Two categories of DRM technologies have evolved in digital signal processing recently, namely digital fingerprinting and digital watermarking. One area of Copyright that is consistently overlooked in DRM developments is 'Public Performance'. The research described in this thesis analysed the administration of public performance rights within the music industry in general, with specific focus on the collective rights and broadcasting sectors in Ireland. Limitations in the administration of artists' rights were identified. The impact of these limitations on the careers of developing artists was evaluated. A digital audio watermarking scheme is proposed that would meet the requirements of both the broadcast and collective rights sectors. The goal of the scheme is to embed a standard identifier within an audio signal via modification of its spectral properties in such a way that it would be robust and perceptually transparent. Modification of the audio signal spectrum was attempted in a variety of ways. A method based on a super-resolution frequency identification technique was found to be most effective. The watermarking scheme was evaluated for robustness and found to be extremely effective in recovering embedded watermarks in music signals using a semi-blind decoding process. The final digital audio watermarking algorithm proposed facilitates the development of other applications in the domain of broadcast monitoring for the purposes of equitable royalty distribution along with additional applications and extension to other domains

Learning Robust Radio Frequency Fingerprints Using Deep Convolutional Neural Networks

Radio Frequency Fingerprinting (RFF) techniques, which attribute uniquely identifiable signal distortions to emitters via Machine Learning (ML) classifiers, are limited by fingerprint variability under different operational conditions. First, this work studied the effect of frequency channel for typical RFF techniques. Performance characterization using the multi-class Matthews Correlation Coefficient (MCC) revealed that using frequency channels other than those used to train the models leads to deterioration in MCC to under 0.05 (random guess), indicating that single-channel models are inadequate for realistic operation. Second, this work presented a novel way of studying fingerprint variability through Fingerprint Extraction through Distortion Reconstruction (FEDR), a neural network-based approach for quantifying signal distortions in a relative distortion latent space. Coupled with a Dense network, FEDR fingerprints were evaluated against common RFF techniques for up to 100 unseen classes, where FEDR achieved best performance with MCC ranging from 0.945 (5 classes) to 0.746 (100 classes), using 73% fewer training parameters than the next-best technique

Physical Layer Defenses Against Primary User Emulation Attacks

Cognitive Radio (CR) is a promising technology that works by detecting unused parts of the spectrum and automatically reconfiguring the communication system\u27s parameters in order to operate in the available communication channels while minimizing interference. CR enables efficient use of the Radio Frequency (RF) spectrum by generating waveforms that can coexist with existing users in licensed spectrum bands. Spectrum sensing is one of the most important components of CR systems because it provides awareness of its operating environment, as well as detecting the presence of primary (licensed) users of the spectrum

Automatic Wi-Fi Fingerprint System based on Unsupervised Learning

Recently, smartphones and Wi-Fi appliances have been generalized in daily life, and location-based service(LBS) has gradually been extended to indoor environments. Unlike outdoor positioning, which is typically handled by the global positioning system(GPS), indoor positioning technologies for providing LBSs have been studied with algorithms using various short-range wireless communications such as Wi-Fi, Ultra-wideband, Bluetooth, etc. Fingerprint-based positioning technology, a representative indoor LBS, estimates user locations using the received signal strength indicator(RSSI), indicating the relative transmission power of the access point(AP). Therefore, a fingerprint-based algorithm has the advantage of being robust to distorted wireless environments, such as radio wave reflections and refractions, compared to the time-of-arrival(TOA) method for non-line-of-sight(NLOS), where many obstacles exist. Fingerprint is divided into a training phase in which a radio map is generated by measuring the RSSIs of all indoor APs and positioning phase in which the positions of users are estimated by comparing the RSSIs of the generated radio map in real-time. In the training phase, the user collects the RSSIs of all APs measured at reference points set at regular intervals of 2 to 3m, creating a radio map. In the positioning phase, the reference point, which is most similar to the RSSI, compares the generated radio map from the training phase to the RSSI measured from user movements. This estimates the real-time indoor position. Fingerprint algorithms based on supervised and semi-supervised learning such as support vector machines and principal component analysis are essential for measuring the RSSIs in all indoor areas to produce a radio map. As the building size and the complexity of structures increases, the amount of work and time required also increase. The radio map generation algorithm that uses channel modeling does not require direct measurement, but it requires considerable effort because of building material, three-dimensional reflection coefficient, and numerical modeling of all obstacles. To overcome these problems, this thesis proposes an automatic Wi-Fi fingerprint system that combines an unsupervised dual radio mapping(UDRM) algorithm that reduces the time taken to acquire Wi-Fi signals and leverages an indoor environment with a minimum description length principle(MDLP)-based radio map feedback(RMF) algorithm to simultaneously optimize and update the radio map. The proposed UDRM algorithm in the training phase generates a radio map of the entire building based on the measured radio map of one reference floor by selectively applying the autoencoder and the generative adversarial network(GAN) according to the spatial structures. The proposed learning-based UDRM algorithm does not require labeled data, which is essential for supervised and semi-supervised learning algorithms. It has a relatively low dependency on RSSI datasets. Additionally, it has a high accuracy of radio map prediction than existing models because it learns the indoor environment simultaneously via a indoor two-dimensional map(2-D map). The produced radio map is used to estimate the real-time positioning of users in the positioning phase. Simultaneously, the proposed MDLP-based RMF algorithm analyzes the distribution characteristics of the RSSIs of newly measured APs and feeds the analyzed results back to the radio map. The MDLP, which is applied to the proposed algorithm, improves the performance of the positioning and optimizes the size of the radio map by preventing the indefinite update of the RSSI and by updating the newly added APs to the radio map. The proposed algorithm is compared with a real measurement-based radio map, confirming the high stability and accuracy of the proposed fingerprint system. Additionally, by generating a radio map of indoor areas with different structures, the proposed system is shown to be robust against the change in indoor environment, thus reducing the time cost. Finally, via a euclidean distance-based experiment, it is confirmed that the accuracy of the proposed fingerprint system is almost the same as that of the RSSI-based fingerprint system.|최근 스마트폰과 Wi-Fi가 실생활에 보편화되면서 위치기반 서비스에 대한 개발 분야가 실내 환경으로 점차 확대되고 있다. GPS로 대표되는 실외 위치 인식과 달리 위치기반 서비스를 제공하기 위한 실내 위치 인식 기술은 Wi-Fi, UWB, 블루투스 등과 같은 다양한 근거리 무선 통신 기반의 알고리즘들이 연구되고 있다. 대표적인 실내 위치인식 알고리즘 중 하나인 Fingerprint는 사용자가 수신한 AP 신호의 상대적인 크기를 나타내는 RSSI를 사용하여 위치를 추정한다. 따라서 Fingerprint기반의 알고리즘은 장애물이 많이 존재하는 비가시 거리에서 TOA 방식에 비해 전파의 반사 및 굴절과 같이 왜곡된 무선 환경에 강인하다는 장점이 있다. Fingerprint는 실내의 모든 AP의 RSSI들을 측정하여 Radio map을 제작하는 과정인 학습 단계와 생성된 Radio map의 RSSI들을 실시간으로 측정된 RSSI와 비교하여 사용자의 위치를 추정하는 위치인식 단계로 나누어진다. 학습 단계에서는 위치를 구분하기 위하여 사용자가 2~3m의 일정한 간격으로 설정된 참조 위치들마다 측정되는 모든 AP들의 RSSI를 수집하고 Radio map으로 제작한다. 위치인식 단계에서는 학습 단계에서 제작된 Radio map과 사용자의 이동에 의해 측정되는 RSSI의 비교를 통해 가장 유사한 RSSI 패턴을 가지는 참조 위치가 실시간 실내 위치로 추정된다. 서포트 벡터 머신(SVM), 주성분 분석(PCA) 등과 같이 지도 및 준지도 학습기반의 Fingerprint 알고리즘은 Radio map을 제작하기 위해 모든 실내 공간에서 RSSI의 측정이 필수적이다. 이러한 알고리즘들은 건물이 대형화되고 구조가 복잡해질수록 측정 공간이 늘어나면서 작업과 시간 소모가 또한 급격히 증가한다. 채널모델링을 통한 Radio map 생성 알고리즘은 직접적인 측정 과정이 불필요한 반면에 건물의 재질, 3차원적인 구조에 따른 반사 계수 및 모든 장애물에 대한 수치적인 모델링이 필수적이기 때문에 상당히 많은 작업량이 요구된다. 따라서 본 논문에서는 이러한 문제점들을 해결하고자 학습 단계에서 Wi-Fi 신호의 수집시간을 최소화하면서 실내 환경이 고려된 Unsupervised Dual Radio Mapping(UDRM) 알고리즘과 위치인식 단계에서 Radio map의 최적화가 동시에 가능한 Minimum description length principle(MDLP)기반의 Radio map Feedback(RMF) 알고리즘이 결합된 비지도학습기반의 자동 Wi-Fi Fingerprint를 제안한다. 학습 단계에서 제안하는 UDRM 알고리즘은 뉴럴 네트워크 기반의 비지도 학습 알고리즘인 Autoencoder와 Generative Adversarial Network (GAN)를 공간구조에 따라 선택적으로 적용하여 하나의 참조 층에서 측정된 Radio map을 기반으로 건물전체의 Radio map을 생성한다. 제안하는 비지도 학습 기반 UDRM 알고리즘은 지도 및 준지도 학습에서 필수적인 Labeled data가 필요하지 않으며 RSSI 데이터 세트의 의존성이 상대적으로 낮다. 또한 2차원 실내 지도를 통해 실내 환경을 동시에 학습하기 때문에 기존의 예측 모델에 비해 Radio map의 예측 정확도가 높다. 제안한 알고리즘에 의해 제작된 Radio map은 위치인식 단계에서 사용자의 실시간 위치인식에 적용된다. 동시에 제안하는 MDLP 기반의 자동 Wi-Fi 업데이트 알고리즘은 새롭게 측정되는 AP들의 RSSI의 분포특성을 분석하고 그 결과를 Radio map에 피드백한다. 제안한 알고리즘에 적용된 MDLP는 무분별한 RSSI의 업데이팅을 방지하고 추가되는 AP를 Radio map에 업데이트함으로서 위치인식의 성능을 향상시키고 Radio map의 크기의 최적화가 가능하다. 제안한 알고리즘은 실제 측정기반의 Radio map과 서로 비교를 통해 제안한 Fingerprint 시스템의 높은 안정성과 정확도를 확인하였다. 또한 구조가 다른 실내공간의 Radio map 생성 결과를 통해 실내 환경 변화에 강인함과 학습 시간 측정을 통한 시간 비용이 감소함을 확인하였다. 마지막으로 Euclidean distance 기반 실험을 통하여 실제 측정한 RSSI기반의 Fingerprint 시스템과 제안한 시스템의 위치인식 정확도가 거의 일치함을 확인하였다.Contents Contents ⅰ Lists of Figures and Tables ⅲ Abstract ⅵ Chapter 1 Introduction 01 1.1 Background and Necessity for Research 01 1.2 Objectives and Contents for Research 04 Chapter 2 Wi-Fi Positioning and Unsupervised Learning 07 2.1 Wi-Fi Positioning 07 2.1.1 Wi-Fi Signal and Fingerprint 07 2.1.2 Fingerprint Techniques 15 2.2 Unsupervised Learning 23 2.2.1 Neural Network 23 2.2.2 Autoencoder 28 2.2.3 Generative Adversarial Network 31 Chapter 3 Proposed Fingerprint System 36 3.1 Unsupervised Dual Radio Mapping Algorithm 36 3.2 MDLP-based Radio Map Feedback Algorithm 47 Chapter 4 Experiment and Result 51 4.1 Experimental Environment and Configuration 51 4.2 Results of Unsupervised Dual Radio Mapping Algorithm 56 4.2 Results of MDLP-based Radio Map Feedback Algorithm 69 Chapter 5 Conclusion 79 Reference 81Docto