Additive Distortion Research Articles

Model-based image steganography using asymmetric embedding scheme

Recently, some steganographic schemes, which take into account the interaction among adjacent modifications, have emerged to tackle the issue of nonadditive embedding and exhibited higher security when compared with the original additive schemes. However, the performances of these schemes are volatile and very sensitive to both the heuristic parameters and the choice of initial additive distortion functions. We proposed a model-based steganographic scheme, which incorporates the adjacent embedding information without relying on any heuristic parameters. Following the spirit of prior arts, the proposed scheme divides the cover image into several interleaved sublattices and embeds messages on each sublattice sequentially. Adjacent modifications are utilized as a priori knowledge to optimize the asymmetric change probabilities of modifying each pixel by +1 and −1. Experimental results show that the proposed scheme can rival or outperform the prior arts and, moreover, maintains a stable security performance, irrespective of any initial additive distortion or any steganalysis detector.

Dimension reduction regressions with measurement errors subject to additive distortion

ABSTRACTIn this paper, we propose several dimension reduction methods when the covariates are measured with additive distortion measurement errors. These distortions are modelled by unknown functions of a commonly observable confounding variable. To estimate the central subspace, we propose residuals-based dimension reduction estimation methods and direct estimation methods. The consistency and asymptotic normality of the proposed estimators are investigated. Furthermore, we conduct some simulations to evaluate the performance of our proposed method and compare with existing methods, and a real data set is analysed for illustration.

Secret data sharing using steganography and image processing

Steganography is a technique that helps to hide the secret data inside the digitally covered image. The message to be hidden can be a text,image, speech, video. The proposed method aims to combine the technique of steganography and Image Processing. Cover image helps to carry the secret data in an embedded form which is referred as stego image. This work proposes a new implementation process called clustering modification directions (CMDs). To implement this methodology, several sub images will be created by decomposing the cover image using additive distortion functions. To enhance the security, password protection is also applied for the hidden information to be retrieved.

Nonlinear measurement errors models subject to partial linear additive distortion

We study nonlinear regression models when the response and predictors are unobservable and distorted in a multiplicative fashion by partial linear additive models (PLAM) of some observed confounding variables. After approximating the additive nonparametric components in the PLAM via polynomial splines and calibrating the unobserved response and unobserved predictors, we develop a semi-parametric profile nonlinear least squares procedure to estimate the parameters of interest. The resulting estimators are shown to be asymptotically normal. To construct confidence intervals for the parameters of interest, an empirical likelihood-based statistic is proposed to improve the accuracy of the associated normal approximation. We also show that the empirical likelihood statistic is asymptotically chi-squared. Moreover, a test procedure based on the empirical process is proposed to check whether the parametric regression model is adequate or not. A wild bootstrap procedure is proposed to compute $p$-values. Simulation studies are conducted to examine the performance of the estimation and testing procedures. The methods are applied to re-analyze real data from a diabetes study for an illustration.

Holographic measurement of distortion during laser melting: Additive distortion from overlapping pulses

Laser - material interactions such as welding, heat treatment and thermal bending generate thermal gradients which give rise to thermal stresses and strains which often result in a permanent distortion of the heated object. This paper investigates the thermal distortion response which results from pulsed laser surface melting of a stainless steel sheet. Pulsed holography has been used to accurately monitor, in real time, the out-of-plane distortion of stainless steel samples melted on one face by with both single and multiple laser pulses. It has been shown that surface melting by additional laser pulses increases the out of plane distortion of the sample without significantly increasing the melt depth. The distortion differences between the primary pulse and subsequent pulses has also been analysed for fully and partially overlapping laser pulses.

An AMR adaptive steganography algorithm based on minimizing distortion

To improve the concealment and statistics security, the paper proposed an AMR (Adaptive Multi-Rate) FCB (Fixed CodeBook) Adaptive steganography scheme (AFA), which is based on the search principle of AMR FCB and the correlation of non-zero pulse positions. The key contribution of the scheme is the design of the cost function and the additive distortion function. The optimal probability of pulse and the pulse correlation in the same track were introduced to cost function to improve the statistics security of the proposed algorithm. The hit function b(n) which is used to search for optimal pulse position was added to additive distortion function to improve the concealment of the algorithm. The experiment results show that the proposed scheme has better hearing concealment and security performance to resist the detecting of the existing steganalysis algorithms than the other steganography schemes.

Text-independent speaker identification using robust statistics estimation

It is well-known that the performance of Gaussian mixture model-based text-independent speaker identification systems deteriorates significantly with the presence of noise and spectral distortion in the training and testing utterances. In this paper, we propose a novel GMM-based speaker identification system based on two robust-statistics estimation methods: the minimum volume ellipsoid method, and the minimum covariance determinant method. Compared to the traditional maximum likelihood estimation method, the proposed methods are less sensitive to outliers in the feature-vector space caused by additive noise and spectral distortion. Moreover, in the testing phase, we propose a simple distance metric to be used for comparing the unknown testing utterance against the speakers’ models. Furthermore, we derive a more robust version of the i-vector extractor, named robust i-vector, which utilizes our proposed robust estimation methods for estimating the parameters of the base universal background model. The proposed classification system has been applied to the NIST 2000 speaker recognition evaluation and the COSINE database. It has also been compared against state-of-the-art techniques such as the GMM/UBM method, the super-vectors method, and the i-vector methods. Experimental results show that the proposed classification system provides up to 16% relative improvement in the identification performance over the i-vector methods for short utterances in the NIST 2000 database and up to 8% when the utterances of the NIST 2000 database are contaminated by different types of artificial noise for signal-to-noise ratio ranging from 0 to 20 dB. For the COSINE database, the robust i-vector estimation provides an absolute improvement of up to 8%. Finally, the real time factor of the proposed distance metric for testing is 55% higher than the RT of the regular ML scoring.

Time for dithering: fast and quantized random embeddings via the restricted isometry property

Recently, many works have focused on the characterization of non-linear dimensionality reduction methods obtained by quantizing linear embeddings, e.g., to reach fast processing time, efficient data compression procedures, novel geometry-preserving embeddings or to estimate the information/bits stored in this reduced data representation. In this work, we prove that many linear maps known to respect the restricted isometry property (RIP) can induce a quantized random embedding with controllable multiplicative and additive distortions with respect to the pairwise distances of the data points beings considered. In other words, linear matrices having fast matrix-vector multiplication algorithms (e.g., based on partial Fourier ensembles or on the adjacency matrix of unbalanced expanders) can be readily used in the definition of fast quantized embeddings with small distortions. This implication is made possible by applying right after the linear map an additive and random dither that stabilizes the impact of the uniform scalar quantization operator applied afterwards. For different categories of RIP matrices, i.e., for different linear embeddings of a metric space $(\mathcal K \subset \mathbb R^n, \ell_q)$ in $(\mathbb R^m, \ell_p)$ with $p,q \geq 1$, we derive upper bounds on the additive distortion induced by quantization, showing that it decays either when the embedding dimension $m$ increases or when the distance of a pair of embedded vectors in $\mathcal K$ decreases. Finally, we develop a novel bi-dithered quantization scheme, which allows for a reduced distortion that decreases when the embedding dimension grows and independently of the considered pair of vectors.

Rate-Splitting to Mitigate Residual Transceiver Hardware Impairments in Massive MIMO Systems

Rate-splitting (RS) has recently been shown to provide significant performance benefits in various multiuser transmission scenarios. In parallel, the huge degrees-of-freedom provided by the appealing massive multiple-input multiple-output (MIMO) necessitate the employment of inexpensive hardware, being more prone to hardware imperfections, in order to be a cost-efficient technology. Hence, in this paper, we focus on a realistic massive multiple-input single-output broadcast channel hampered by the inevitable hardware impairments. We consider a general experimentally validated model of hardware impairments, accounting for the presence of multiplicative distortion due to phase noise, additive distortion noise and thermal noise amplification. Under both scenarios with perfect and imperfect channel state information at the transmitter (CSIT), we analyze the potential robustness of RS to each separate hardware imperfection. We analytically assess the sum-rate degradation due to hardware imperfections. Interestingly, in the case of imperfect CSIT, we demonstrate that RS is a robust strategy for multiuser MIMO in the presence of phase and amplified thermal noise, since its sum-rate does not saturate at high signal-to-noise ratio (SNR), contrary to conventional techniques. On the other hand, the additive impairments always lead to a sum-rate saturation at high SNR, even after the application of RS. However, RS still enhances the performance. Furthermore, as the number of users increases, the gains provided by RS decrease not only in ideal conditions, but in practical conditions with residual transceiver hardware impairments as well. Notably, although a deterministic equivalent analysis is employed, the analytical and simulation results coincide even for finite system dimensions. As a consequence, the applicability of these results also holds for current “small scale” multiantenna systems.

Estimation and hypothesis test on partial linear models with additive distortion measurement errors

We consider estimation and hypothesis test for partial linear measurement errors models when the response variable and covariates in the linear part are measured with additive distortion measurement errors, which are unknown functions of a commonly observable confounding variable. We propose a transformation based profile least squares estimator to estimate unknown parameter under unrestricted and restricted conditions. Asymptotic properties for the estimators are established. To test a hypothesis on the parametric components, a test statistic based on the normalized difference between the residual sums of squares under the null and alternative hypotheses is proposed, and we further show that its limiting distribution is a standard chi-squared distribution. Lastly, we suggest a lack-of-fit test of score type for checking the validity of partial linear models. The quadratic form of the scaled test statistic is asymptotically chi-squared under the null hypothesis and a non-centered one under local alternatives converging to the null hypothesis at parametric rates. Simulation studies are conducted to demonstrate the performance of the proposed procedure and a real example is analyzed for an illustration.

Analysis and Design of Secure Massive MIMO Systems in the Presence of Hardware Impairments

To keep the hardware costs of future communications systems manageable, the use of low-cost hardware components is desirable. This is particularly true for the emerging massive multiple-input multiple-output (MIMO) systems which equip base stations (BSs) with a large number of antenna elements. However, low-cost transceiver designs will further accentuate the hardware impairments, which are present in any practical communication system. In this paper, we investigate the impact of hardware impairments on the secrecy performance of downlink massive MIMO systems in the presence of a passive multiple-antenna eavesdropper. Thereby, for the BS and the legitimate users, the joint effects of multiplicative phase noise, additive distortion noise, and amplified receiver noise are taken into account, whereas the eavesdropper is assumed to employ ideal hardware. We derive a lower bound for the ergodic secrecy rate of a given user when matched filter data precoding and artificial noise (AN) transmission are employed at the BS. Based on the derived analytical expression, we investigate the impact of the various system parameters on the secrecy rate and optimize both the pilot sets used for uplink training and the AN precoding. Our analytical and simulation results reveal that: 1) the additive distortion noise at the BS may be beneficial for the secrecy performance, especially if the power assigned for AN emission is not sufficient; 2) all other hardware impairments have a negative impact on the secrecy performance; 3) despite their susceptibility to pilot interference in the presence of phase noise, so-called spatially orthogonal pilot sequences are preferable unless the phase noise is very strong; and 4) the proposed generalized null-space AN precoding method can efficiently mitigate the negative effects of phase noise.

Partial linear single-index models with additive distortion measurement errors

ABSTRACTWe study partial linear single-index models (PLSiMs) when the response and the covariates in the parametric part are measured with additive distortion measurement errors. These distortions are modeled by unknown functions of a commonly observable confounding variable. We use the semiparametric profile least-squares method to estimate the parameters in the PLSiMs based on the residuals obtained from the distorted variables and confounding variable. We also employ the smoothly clipped absolute deviation penalty (SCAD) to select the relevant variables in the PLSiMs. We show that the resulting SCAD estimators are consistent and possess the oracle property. For the non parametric link function, we construct the simultaneous confidence bands and obtain the asymptotic distribution of the maximum absolute deviation between the estimated link function and the true link function. A simulation study is conducted to evaluate the performance of the proposed methods and a real dataset is analyzed for illustration.

Bayesian feature enhancement using independent vector analysis and reverberation parameter re-estimation for noisy reverberant speech recognition

Because speech recorded by distant microphones in real-world environments is contaminated by both additive noise and reverberation, the automatic speech recognition (ASR) performance is seriously degraded due to the mismatch between the training and testing environments. In the previous studies, some of the authors proposed a Bayesian feature enhancement (BFE) method with re-estimation of reverberation filter parameters for reverberant speech recognition and a BFE method employing independent vector analysis (IVA) to deal with speech corrupted by additive noise. Although both of them accomplish significant improvements in either reverberation- or noise-robust ASR, most of the real-world environments involve both additive noise and reverberation. For robust ASR in the noisy reverberant environments, in this paper, we present a hidden-Markov-model (HMM)-based BFE method using IVA and reverberation parameter re-estimation (RPR) to remove additive and reverberant distortion components in speech acquired by multi-microphones effectively by introducing Bayesian inference in the observation model of input speech features. Experimental results show that the presented method can further reduce the word error rates (WERs) compared with the BFE methods based on conventional noise and/or reverberation models and combinations of the BFE methods for reverberation- or noise-robust ASR.

The A Priori Knowledge Based Secure Payload Estimation for Additive Model

In this paper, we propose a practical method to estimate the payload rate for individual cover before stego embedding. The proposed method is adopted to the additive distortion model. The a priori knowledge functions employed in the method contains the relation function of steganalyzer's detection error and stego distortion (PE– D), and the relation function of payload rate and distortion(D–α) of the given cover. As it is not suitable to measure the stego security with stego distortion, we adopt PE as the security metric. With the sender's expected PE, the role of PE– D function is to calculate the corresponding D, and then sender can solve out his expectedα with D–α function for the cover. The PE – D function is acquired before estimating phase. During the estimating, the most time-consuming part is calculating the D–α function for the cover, which costs 1 time of stego embedding. Our method is an efficient solution for estimating the secure payload rate.

Comparison of single-microphone noise reduction schemes: can hearing impaired listeners tell the difference?

Objective: The perceived qualities of nine different single-microphone noise reduction (SMNR) algorithms were to be evaluated and compared in subjective listening tests with normal hearing and hearing impaired (HI) listeners. Design: Speech samples added with traffic noise or with party noise were processed by the SMNR algorithms. Subjects rated the amount of speech distortions, intrusiveness of background noise, listening effort and overall quality, using a simplified MUSHRA (ITU-R, 2003) assessment method. Study sample: 18 normal hearing and 18 moderately HI subjects participated in the study. Results: Significant differences between the rating behaviours of the two subject groups were observed: While normal hearing subjects clearly differentiated between different SMNR algorithms, HI subjects rated all processed signals very similarly. Moreover, HI subjects rated speech distortions of the unprocessed, noisier signals as being more severe than the distortions of the processed signals, in contrast to normal hearing subjects. Conclusions: It seems harder for HI listeners to distinguish between additive noise and speech distortions or/and they might have a different understanding of the term “speech distortion” than normal hearing listeners have. The findings confirm that the evaluation of SMNR schemes for hearing aids should always involve HI listeners.

Small Width, Low Distortions: Quantized Random Embeddings of Low-complexity Sets

Under which conditions and with which distortions can we preserve the pairwise distances of low-complexity vectors, e.g., for structured sets , such as the set of sparse vectors or the one of low-rank matrices, when these are mapped (or embedded) in a finite set of vectors? This work addresses this general question through the specific use of a quantized and dithered random linear mapping, which combines, in the following order, a sub-Gaussian random projection in $\mathbb R^{M}$ of vectors in $\mathbb R^{N}$ , a random translation, or dither , of the projected vectors, and a uniform scalar quantizer of resolution $\delta >0$ applied componentwise. Thanks to this quantized mapping, we are first able to show that, with high probability, an embedding of a bounded set $\mathcal K \subset \mathbb R^{N}$ in $\delta \mathbb Z^{M}$ can be achieved when distances in the quantized and in the original domains are measured with the $\ell _{1}$ - and $\ell _{2}$ -norm, respectively, and provided the number of quantized observations $M$ is large before the square of the “Gaussian mean width” of $\mathcal K$ . In this case, we show that the embedding is actually quasi-isometric and only suffers from both multiplicative and additive distortions whose magnitudes decrease as $M^{-1/5}$ for general sets, and as $M^{-1/2}$ for structured set, when $M$ increases. Second, when one is only interested in characterizing the maximal distance separating two elements of $\mathcal K$ mapped to the same quantized vector, i.e., the “consistency width” of the mapping, we show that for a similar number of measurements and with high probability, this width decays as $M^{-1/4}$ for general sets and as $1/M$ for structured ones when $M$ increases. Finally, as an important aspect of this paper, we also establish how the non-Gaussianity of sub-Gaussian random projections inserted in the quantized mapping ( e.g. , for Bernoulli random matrices) impacts the class of vectors that can be embedded or whose consistency width provably decays when $M$ increases.

Experimental Study on Extreme Learning Machine Applications for Speech Enhancement

In wireless telephony and audio data mining applications, it is desirable that noise suppression can be made robust against changing noise conditions and operates in real time (or faster). The learning effectiveness and speed of artificial neural networks are therefore critical factors in applications for speech enhancement tasks. To address these issues, we present an extreme learning machine (ELM) framework, aimed at the effective and fast removal of background noise from a single-channel speech signal, based on a set of randomly chosen hidden units and analytically determined output weights. Because feature learning with shallow ELM may not be effective for natural signals, such as speech, even with a large number of hidden nodes, hierarchical ELM (H-ELM) architectures are deployed by leveraging sparse autoencoders. In this manner, we not only keep all the advantages of deep models in approximating complicated functions and maintaining strong regression capabilities, but we also overcome the cumbersome and time-consuming features of both greedy layer-wise pre-training and back-propagation (BP)-based fine tuning schemes, which are typically adopted for training deep neural architectures. The proposed ELM framework was evaluated on the Aurora-4 speech database. The Aurora-4 task provides relatively limited training data, and test speech data corrupted with both additive noise and convolutive distortions for matched and mismatched channels and signal-to-noise ratio (SNR) conditions. In addition, the task includes a subset of testing data involving noise types and SNR levels that are not seen in the training data. The experimental results indicate that when the amount of training data is limited, both ELMand H-ELM-based speech enhancement techniques consistently outperform the conventional BP-based shallow and deep learning algorithms, in terms of standardized objective evaluations, under various testing conditions.

Feedback-Driven Sensory Mapping Adaptation for Robust Speech Activity Detection.

Parsing natural acoustic scenes using computational methodologies poses many challenges. Given the rich and complex nature of the acoustic environment, data mismatch between train and test conditions is a major hurdle in data-driven audio processing systems. In contrast, the brain exhibits a remarkable ability at segmenting acoustic scenes with relative ease. When tackling challenging listening conditions that are often faced in everyday life, the biological system relies on a number of principles that allow it to effortlessly parse its rich soundscape. In the current study, we leverage a key principle employed by the auditory system: its ability to adapt the neural representation of its sensory input in a high-dimensional space. We propose a framework that mimics this process in a computational model for robust speech activity detection. The system employs a 2-D Gabor filter bank whose parameters are retuned offline to improve the separability between the feature representation of speech and nonspeech sounds. This retuning process, driven by feedback from statistical models of speech and nonspeech classes, attempts to minimize the misclassification risk of mismatched data, with respect to the original statistical models. We hypothesize that this risk minimization procedure results in an emphasis of unique speech and nonspeech modulations in the high-dimensional space. We show that such an adapted system is indeed robust to other novel conditions, with a marked reduction in equal error rates for a variety of databases with additive and convolutive noise distortions. We discuss the lessons learned from biology with regard to adapting to an ever-changing acoustic environment and the impact on building truly intelligent audio processing systems.

Source Distinguishability Under Distortion-Limited Attack: An Optimal Transport Perspective

We analyze the distinguishability of two sources in a Neyman–Pearson setup when an attacker is allowed to modify the output of one of the two sources subject to an additive distortion constraint. By casting the problem in a game-theoretic framework and by exploiting the parallelism between the attacker’s goal and optimal transport theory, we introduce the concept of security margin defined as the maximum average per-sample distortion introduced by the attacker for which the two sources can be distinguished ensuring arbitrarily small, yet positive, error exponents for type I and type II error probabilities. Several versions of the problem are considered according to the available knowledge about the sources. We compute the security margin for some classes of sources and derive general bounds assuming that the distortion is measured in terms of the mean square error between the original and the attacked sequence. The analysis of the game and the study of the distinguishability of the sources are extended to the case in which the distortion constraint is defined in terms of the maximum distance.

Correlation analysis with additive distortion measurement errors

ABSTRACTThis paper studies the estimation of correlation coefficient between unobserved variables of interest. These unobservable variables are distorted in a additive fashion by an observed confounding variable. Two estimators, a direct-plug-in estimator and a residual-based estimator, are proposed. Their asymptotical results are obtained, and the residual-based estimator is shown asymptotically efficient. Moreover, we suggest an asymptotic normal approximation and an empirical likelihood-based statistic to construct the confidence interval. The empirical likelihood statistic is shown to be asymptotically chi-squared. Simulation studies are conducted to examine the performance of the proposed estimators. These methods are applied to analyse the Boston housing price data for an illustration.