Biblio

Recently, there has been significant enthusiasms in exploiting physical (PHY-) layer characteristics for secure wireless communication. However, most existing PHY-layer security paradigms are information theoretical methodologies, which are infeasible to real and practical systems. In this paper, we propose a weighted fractional Fourier transform (WFRFT) pre-coding scheme to enhance the security of wireless transmissions against eavesdropping. By leveraging the concept of WFRFT, the proposed scheme can easily change the characteristics of the underlying radio signals to complement and secure upper-layer cryptographic protocols. We demonstrate a running prototype based on the LTE-framework. First, the compatibility between the WFRFT pre-coding scheme and the conversational LTE architecture is presented. Then, the security mechanism of the WFRFT pre-coding scheme is demonstrated. Experimental results validate the practicability and security performance superiority of the proposed scheme.

Previous research on frequency hopping (FH) signal recognition utilizing deep learning only focuses on single-label signal, but can not deal with overlapped FH signal which has multi-labels. To solve this problem, we propose a new FH signal recognition method based on fully convolutional networks (FCN). Firstly, we perform the short-time Fourier transform (STFT) on the collected FH signal to obtain a two-dimensional time-frequency pattern with time, frequency, and intensity information. Then, the pattern will be put into an improved FCN model, named FH-FCN, to make a pixel-level prediction. Finally, through the statistics of the output pixels, we can get the final classification results. We also design an algorithm that can automatically generate dataset for model training. The experimental results show that, for an overlapped FH signal, which contains up to four different types of signals, our method can recognize them correctly. In addition, the separation of multiple FH signals can be achieved by a slight improvement of our method.

The accurate classification of WiFi-based activity patterns is still an open problem and is critical to detect behavior for non-visualization applications. This paper proposes a novel approach that uses WiFi-based IQ data and short-time Fourier transform (STFT) time-frequency images to automatically and accurately classify human activities. The offsets features, calculated from time-domain values and one-dimensional principal component analysis (1D-PCA) values and two-dimensional principal component analysis (2D-PCA) values, are applied as features to input the classifiers. The machine learning methods such as the bagging, boosting, support vector machine (SVM), random forests (RF) as the classifier to output the performance. The experimental data validate our proposed method with 15000 experimental samples from five categories of WiFi signals (empty, marching on the spot, rope skipping, both arms rotating;singlearm rotating). The results show that the method companying with the RF classifier surpasses the approach with alternative classifiers on classification performance and finally obtains a 62.66% classification rate, 85.06% mean accuracy, and 90.67% mean specificity.

Completely Automated Public Turing Tests (CAPTCHA) have been used to differentiate between computers and humans for quite some time now. There are many different varieties of CAPTCHAs - text-based, image-based, audio, video, arithmetic, etc. However, not all varieties are suitable for the visually impaired. As time goes by and Spambots and APIs grow more accurate, the CAPTCHA tests have been constantly updated to stay relevant, but that has not happened with the audio CAPTCHA. There exists an audio CAPTCHA intended for the blind/visually impaired but many blind/visually impaired find it difficult to solve. We propose an alternative to the existing system, which would make use of unique sound samples layered with music generated through GANs (Generative Adversarial Networks) along with noise and other layers of sounds to make it difficult to dissect. The user has to count the number of times the unique sound was heard in the sample and then input that number. Since there are no letters or numbers involved in the samples, speech-to-text bots/APIs cannot be used directly to decipher this system. Also, any user regardless of their native language can comfortably use this system.

Optical time domain reflectometry (OTDR) plays an important role in optical fiber communications. To improve the performance of OTDR, we propose a method based on the Variational Mode Decomposition (VMD) and Hilbert transform (HT) for fiber events detection. Firstly, the variational mode decomposition is applied to decompose OTDR data into some intrinsic mode functions (imfs). To determine the decomposition mode number in VMD, an adaptive estimation method is introduced. Secondly, the Hilbert transform is utilized to obtain the instantaneous amplitude of the imf for events localization. Finally, the Dynamic Time Warping (DTW) is used for identifying the type of event. Experimental results show that the proposed method can locate events accurately. Compared with the Short-Time Fourier Transform (STFT) method, the VMD-HT method presents a higher accuracy in events localization, which indicates that the method is effective and applicable.

Attestation of audio signals for recognition of forgery in voice is challenging task. In this research work, a deep convolutional neural network (CNN) is utilized to detect audio operations i.e. pitch shifted and amplitude varied signals. Short-time Fourier transform (STFT) and Modified Discrete Cosine Transform (MDCT) features are chosen for audio processing and their plotted patterns are fed to CNN. Experimental results show that our model can successfully distinguish tampered signals to facilitate the audio authentication on TIMIT dataset. Proposed CNN architecture can distinguish spoofed voices of shifting pitch with accuracy of 97.55% and of varying amplitude with accuracy of 98.85%.

Most anti-collusion audio fingerprinting schemes are aiming at finding colluders from the illegal redistributed audio copies. However, the loss caused by the redistributed versions is inevitable. In this letter, a novel fingerprinting scheme is proposed to eliminate the motivation of collusion attack. The audio signal is transformed to the frequency domain by the Fourier transform, and the coefficients in frequency domain are reversed in different degrees according to the fingerprint sequence. Different from other fingerprinting schemes, the coefficients of the host media are excessively modified by the proposed method in order to reduce the quality of the colluded version significantly, but the imperceptibility is well preserved. Experiments show that the colluded audio cannot be reused because of the poor quality. In addition, the proposed method can also resist other common attacks. Various kinds of copyright risks and losses caused by the illegal redistribution are effectively avoided, which is significant for protecting the copyright of audio.

In this study we propose a novel method for drone surveillance that can simultaneously analyze time-frequency responses in all pixels of a high-frame-rate video. The propellers of flying drones rotate at hundreds of Hz and their principal vibration frequency components are much higher than those of their background objects. To separate the pixels around a drone's propellers from its background, we utilize these time-series features for vibration source localization with pixel-level short-time Fourier transform (STFT). We verify the relationship between the number of taps in the STFT computation and the performance of our algorithm, including the execution time and the localization accuracy, by conducting experiments under various conditions, such as degraded appearance, weather, and defocused blur. The robustness of the proposed algorithm is also verified by localizing a flying multi-copter in real-time in an outdoor scenario.

In this paper, new image encryption based on singular value decomposition (SVD), fractional discrete cosine transform (FrDCT) and the chaotic system is proposed for the security of medical image. Reliability, vitality, and efficacy of medical image encryption are strengthened by it. The proposed method discusses the benefits of FrDCT over fractional Fourier transform. The key sensitivity of the proposed algorithm for different medical images inspires us to make a platform for other researchers. Theoretical and statistical tests are carried out demonstrating the high-level security of the proposed algorithm.

To address cybersecurity, this author proposed recently the approach of formulating it in symmetric dual-space and dual-system. This paper further explains this concept, beginning with symmetric Maxwell Equation (ME) and Fourier Transform (FT). The approach appears to be a powerful solution, with wide applications ranging from Electronic Warfare (EW) to 5G Mobile, etc.

With the rapid development of the popularity of wireless networks, there are also increasing security threats that follow, and wireless network security issues are becoming increasingly important. Radio frequency fingerprints generated by device tolerance in wireless device transmitters have physical characteristics that are difficult to clone, and can be used for identity authentication of wireless devices. In this paper, we propose a radio frequency fingerprint extraction method based on fractional Fourier transform for transient signals. After getting the features of the signal, we use RPCA to reduce the dimension of the features, and then use KNN to classify them. The results show that when the SNR is 20dB, the recognition rate of this method is close to 100%.

We present an effective machine learning method for malicious activity detection in enterprise security logs. Our method involves feature engineering, or generating new features by applying operators on features of the raw data. We generate DNF formulas from raw features, extract Boolean functions from them, and leverage Fourier analysis to generate new parity features and rank them based on their highest Fourier coefficients. We demonstrate on real enterprise data sets that the engineered features enhance the performance of a wide range of classifiers and clustering algorithms. As compared to classification of raw data features, the engineered features achieve up to 50.6% improvement in malicious recall, while sacrificing no more than 0.47% in accuracy. We also observe better isolation of malicious clusters, when performing clustering on engineered features. In general, a small number of engineered features achieve higher performance than raw data features according to our metrics of interest. Our feature engineering method also retains interpretability, an important consideration in cyber security applications.

The new criterion for selecting the frequencies of the test polyharmonic signals is developed. It allows uniquely filtering the values of multidimensional transfer functions - Fourier-images of Volterra kernel from the partial component of the response of a nonlinear system. It is shown that this criterion significantly weakens the known limitations on the choice of frequencies and, as a result, reduces the number of interpolations during the restoration of the transfer function, and, the more significant, the higher the order of estimated transfer function.

This paper proposes a novel privacy-preserving smart metering system for aggregating distributed smart meter data. It addresses two important challenges: (i) individual users wish to publish sensitive smart metering data for specific purposes, and (ii) an untrusted aggregator aims to make queries on the aggregate data. We handle these challenges using two main techniques. First, we propose Fourier Perturbation Algorithm (FPA) and Wavelet Perturbation Algorithm (WPA) which utilize Fourier/Wavelet transformation and distributed differential privacy (DDP) to provide privacy for the released statistic with provable sensitivity and error bounds. Second, we leverage an exponential ElGamal encryption mechanism to enable secure communications between the users and the untrusted aggregator. Standard differential privacy techniques perform poorly for time-series data as it results in a Θ(n) noise to answer n queries, rendering the answers practically useless if n is large. Our proposed distributed differential privacy mechanism relies on Gaussian principles to generate distributed noise, which guarantees differential privacy for each user with O(1) error, and provides computational simplicity and scalability. Compared with Gaussian Perturbation Algorithm (GPA) which adds distributed Gaussian noise to the original data, the experimental results demonstrate the superiority of the proposed FPA and WPA by adding noise to the transformed coefficients.

Robust image hashing seeks to transform a given input image into a shorter hashed version using a key-dependent non-invertible transform. These hashes find extensive applications in content authentication, image indexing for database search and watermarking. Modern robust hashing algorithms consist of feature extraction, a randomization stage to introduce non-invertibility, followed by quantization and binary encoding to produce a binary hash. This paper describes a novel algorithm for generating an image hash based on Log-Polar transform features. The Log-Polar transform is a part of the Fourier-Mellin transformation, often used in image recognition and registration techniques due to its invariant properties to geometric operations. First, we show that the proposed perceptual hash is resistant to content-preserving operations like compression, noise addition, moderate geometric and filtering. Second, we illustrate the discriminative capability of our hash in order to rapidly distinguish between two perceptually different images. Third, we study the security of our method for image authentication purposes. Finally, we show that the proposed hashing method can provide both excellent security and robustness.
 

A novel short-time Fourier transform (STFT) domain adaptive filtering scheme is proposed that can be easily combined with nonlinear post filters such as residual echo or noise reduction in acoustic echo cancellation. Unlike normal STFT subband adaptive filters, which suffers from aliasing artifacts due to its poor prototype filter, our scheme achieves good accuracy by exploiting the relationship between the linear convolution and the poor prototype filter, i.e., the STFT window function. The effectiveness of our scheme was confirmed through the results of simulations conducted to compare it with conventional methods.

This paper proposes an algorithm for multi-channel SAR ground moving target detection and estimation using the Fractional Fourier Transform(FrFT). To detect the moving target with low speed, the clutter is first suppressed by Displace Phase Center Antenna(DPCA), then the signal-to-clutter can be enhanced. Have suppressed the clutter, the echo of moving target remains and can be regarded as a chirp signal whose parameters can be estimated by FrFT. FrFT, one of the most widely used tools to time-frequency analysis, is utilized to estimate the Doppler parameters, from which the moving parameters, including the velocity and the acceleration can be obtained. The effectiveness of the proposed method is validated by the simulation.
 

The electric network frequency (ENF) criterion is a recently developed technique for audio timestamp identification, which involves the matching between extracted ENF signal and reference data. For nearly a decade, conventional matching criterion has been based on the minimum mean squared error (MMSE) or maximum correlation coefficient. However, the corresponding performance is highly limited by low signal-to-noise ratio, short recording durations, frequency resolution problems, and so on. This paper presents a threshold-based dynamic matching algorithm (DMA), which is capable of autocorrecting the noise affected frequency estimates. The threshold is chosen according to the frequency resolution determined by the short-time Fourier transform (STFT) window size. A penalty coefficient is introduced to monitor the autocorrection process and finally determine the estimated timestamp. It is then shown that the DMA generalizes the conventional MMSE method. By considering the mainlobe width in the STFT caused by limited frequency resolution, the DMA achieves improved identification accuracy and robustness against higher levels of noise and the offset problem. Synthetic performance analysis and practical experimental results are provided to illustrate the advantages of the DMA.

Large number of digital images and videos are acquired, stored, processed and shared nowadays. High quality imaging hardware and low cost, user friendly image editing software make digital mediums vulnerable to modifications. One of the most popular image modification techniques is copy move forgery. This tampering technique copies part of an image and pastes it into another part on the same image to conceal or to replicate some part of the image. Researchers proposed many techniques to detect copy move forged regions of images recently. These methods divide image into overlapping blocks and extract features to determine similarity among group of blocks. Selection of the feature extraction algorithm plays an important role on the accuracy of detection methods. Column averages of 1D-FT of rows is used to extract features from overlapping blocks on the image. Blocks are transformed into frequency domain using 1D-FT of the rows and average values of the transformed columns form feature vectors. Similarity of feature vectors indicates possible forged regions. Results show that the proposed method can detect copy pasted regions with higher accuracy compared to similar works reported in the literature. The method is also more resistant against the Gaussian blurring or JPEG compression attacks as shown in the results.