Efficient storage and transmission of electromyogram (EMG) data are important for emerging applications such as telemedicine and big data, as a vital tool for further advancement of the field. However, due to limitations in internet speed and hardware resources, transmission and storage of EMG data are challenging. As a solution, this work proposes a new method for EMG data compression using deep convolutional autoencoders (CAE). Eight-channel EMG data from 10 subjects, and high-density EMG data from 18 subjects, were investigated for compression. The CAE architecture was designed to extract an abstract data representation that is heavily compressed, but from which the salient information for classification can be effectively reconstructed. The proposed method attained efficient compression; for CR = 1600, the average PRDN (percentage RMS difference normalized) was 31.5% and the wrist motions classification accuracy (CA) reduced roughly 5%. The CAE substantially outperformed the state-of-the-art high-efficiency video coding and a well-known wavelet-thresholding compression technique. Moreover, by reducing the bit-resolution of the CAE's compressed data from 24 bits to 6 bits, an additional 4-fold compression was achieved without significant degradation of the reconstruction performance. Furthermore, the CAE's inter-subject performance was promising; e.g., for CR = 1600, the PRDN for the inter-subject case was only 2.6% less than that of the within-subject performance. The powerful EMG compression performance with remarkable reconstruction results reflects the CAEs potential as an automatic end-to-end approach with the ability to learn the complete encoding and decoding process. Furthermore, the excellent inter-subject performance demonstrates the generalizability and usability of the proposed approach.

Some methods developed in reversible data hiding (RDH) make use of prediction for data embedding for original pixel estimation. Predicators may also be exploited in RDH in encrypted image (RDHEI); this has become a research interest in recent years because of the development of cloud computing and a need for content owner privacy. This paper presents a high-capacity reversible data hiding in encrypted image (RDHEI) that employs local difference predictor. In this algorithm, an image is divided into non-overlapping blocks. In each block, the central pixel of the block is considered as the leader pixel and others as follower ones. The prediction errors between the intensity of the follower pixels and leader one are calculated using local difference predictor and analyzed to determine a label for block embedding capacity. This label indicates the amount of data that can be embedded in a block after encryption. Using this pre-processing for all blocks, we vacate rooms before the encryption of the original image to achieve high embedding capacity. Also, using these labels, embedded data is extracted and the original image is losslessly reconstructed at the decoding phase. Comparing to existent RDHEI algorithms, not only embedding capacity is increased by the proposed algorithm, but also a perfect reconstruction of the original image is realized by content owner without having data hider key. Experimental results confirm that the proposed algorithm outperforms state of the art RDHEI methods.

The rapid development and growth of cellular mobile networks in recent years has made voice channels widely available almost everywhere. On the other hand, voice dedicated channels possess properties that make them ideal options for high priority real‐time secure communications. Beside these realities, information embedding has gained remarkable attention in recent years due to its potential application in multimedia security. In this regard, this study aims to propose an efficient scheme for data hiding to show the feasibility of exploiting these channels for covert communications. To this end, the authors propose new suboptimal but practical schemes based on additive and multiplicative spread spectrum methods. The analytical evaluations of the proposed techniques along with simulation results show proper efficiency of these schemes. Moreover, it is shown that the proposed scheme based on the M‐ary bi‐orthogonal general maximum likelihood and M‐ary bi‐orthogonal generalised embedding of multiplicative watermarks techniques outperform the other proposed schemes in this study. Experimental evaluation confirms that the proposed data hiding scheme benefits from an acceptable level of imperceptibility. Finally, the authors propose a security block which considerably increases the robustness of proposed structures against histogram‐based steganalysis.

In this study, the authors propose a steganalytic scheme for digital video spread spectrum (SS) data hiding. The proposed method estimates both the hidden message and the gain factor of the SS embedding rules. In this method, the cover frames are first estimated and are compared with the received video frames. Then, the residual matrix is computed and specific features are extracted from this matrix as well as the video frames and estimated frames. The support vector machine, then applies to the extracted features to classify the video as either clean or suspicious. If the video is declared suspicious, both the hidden message and the embedding process gain factor are estimated and consequently the original video is reconstructed. The simulation results confirm the success of the authors’ proposed method in detecting the stego video, estimation of the hidden message and gain factor as well as reconstruction of the original video.

In this study, a group of wireless sensors are tasked to trace indoor obstacles without the need to sense them, directly. The authors introduce a novel framework based on compressed sensing theory that allows sensors to map two‐dimensional spatial details, non‐invasively. By exploiting an alternative projection method which reduces the randomness nature of previous works, the resulting measurement matrix can provide linear samples from an unknown environment more efficiently. It is shown that how sparse representation of the spatial parameters in some domains can be utilised in order to realise obstacle mapping with minimum number of wireless transmissions and receptions. In addition, theoretical analyses along with simulation results illustrate premier performance of their framework. Furthermore, they test their method in different circumstances and show how drawbacks such as walls, bulkheads, and environmental constraints can affect the reconstruction performance. Therefore, they proposed two algorithms (i.e. reducing wall effect and reducing bulkhead effect) in order to decrease the impression of walls and bulkheads which is supported theoretically. Finally, a well‐applicable scenario based on their framework is defined and proposing the next best transmitter algorithm remarkable results are achieved.

The swift growth of cellular mobile networks in recent years has made voice channels almost accessible everywhere. Besides, data hiding has recently attracted significant attention due to its ability to imperceptibly embed side information that can be used for signal enhancement, security improvement, and two-way authentication purposes. In this regard, we aim at proposing efficient schemes for hiding data in the widespread voice channel of cellular networks. To this aim, our first contribution is to model the channel accurately by considering a linear filter plus a nonlinear scaling function. This model is validated through experiments with true speech signals. Then we leverage on this model to propose two additive and multiplicative data hiding methods based on the spread spectrum techniques. In addition, inspired by the concept of M-ary biorthogonal codes, we develop novel schemes that significantly outperform the previous ones. The performance of all the methods that we present is assessed mathematically and cross-validated with simulations. These are later extended to true speech signals where the results evidence an excellent performance as predicted by the theory. Finally, we assess the imperceptibility by means of both subjective and objective benchmarks and show that the perceptual impact of our watermarks is acceptable.

In this study, a blind steganalysis approach which accurately discloses low rate data hiding schemes is proposed. The absolute values of neighbouring joint density (absNJ) are used for feature extraction. In this way, the intra‐ and inter‐block situations are employed providing a variety of different features. Aside from the absolute values of discrete cosine transform (DCT) coefficients, the differential DCT coefficients are also exploited to extract the features. Moreover, the absNJ features will be extended and used over differential DCT coefficients. It is shown that applying the P th power of the DCT coefficients instead of their first power gains more discriminative features. Then, using the ensemble classifier, the cover image is discriminated from the stego one. Experimental results indicate the efficiency of the new scheme in comparison with the previously presented schemes.

Authentication and tampering detection of the digital signals is one of the main applications of the digital watermarking. Recently, watermarking algorithms for digital images are developed to not only detect the image tampering, but also to recover the lost content to some extent. In this paper, a new watermarking scheme is introduced to generate digital self-embedding speech signals enjoying the self-recovery feature. For this purpose, the compressed version of the speech signal generated by a speech codec and protected against the tampering by the proper channel coding is embedded into the original speech signal. Experimental results show that the self-embedding speech signal is recoverable with proper speech quality for high tampering rates, without significant loss in the quality of the original speech signal.

Watermarking algorithms have been widely applied to the field of image forensics recently. One of these very forensic applications is the protection of images against tampering. For this purpose, we need to design a watermarking algorithm fulfilling two purposes in case of image tampering: 1) detecting the tampered area of the received image and 2) recovering the lost information in the tampered zones. State-of-the-art techniques accomplish these tasks using watermarks consisting of check bits and reference bits. Check bits are used for tampering detection, whereas reference bits carry information about the whole image. The problem of recovering the lost reference bits still stands. This paper is aimed at showing that having the tampering location known, image tampering can be modeled and dealt with as an erasure error. Therefore, an appropriate design of channel code can protect the reference bits against tampering. In the present proposed method, the total watermark bit-budget is dedicated to three groups: 1) source encoder output bits; 2) channel code parity bits; and 3) check bits. In watermark embedding phase, the original image is source coded and the output bit stream is protected using appropriate channel encoder. For image recovery, erasure locations detected by check bits help channel erasure decoder to retrieve the original source encoded image. Experimental results show that our proposed scheme significantly outperforms recent techniques in terms of image quality for both watermarked and recovered image. The watermarked image quality gain is achieved through spending less bit-budget on watermark, while image recovery quality is considerably improved as a consequence of consistent performance of designed source and channel codes.

Watemarking the original images to protect them against tampering has recently attracted an overgrowing interest. Recent works in this field offer algorithms that not only localize the tampering, but also recover the original content in the lost area. In this paper, we show that this self-restoration problem can be modeled as a source-channel coding problem. The original image is compressed using an efficient source encoder. The output is then channel coded to be capable of tolerating a certain rate of tampering. At the receiver, decoder reveals the encoder output bit stream if the tampering is below the certain limit. Decoder exploits the location of the erased blocks at the decoding, which are known thanks to the embedded check bits. The output of the source decoder is then used to replace the content of the tampered area. We show that approaching the self-restoration problem from this general viewpoint, the performance is significantly improved comparing to the state of the art schemes, in terms of the quality of watermarked image, quality of the restored content, and tolerable tampering rate.

In this study, a robust image watermarking method based on geometric modelling is presented. Eight samples of wavelet approximation coefficients on each image block are utilised to construct two line segments in the two‐dimensional space. The authors change the angle formed between these line segments for data embedding. Geometrical tools are used to solve the tradeoff between the transparency and robustness of the watermark data. They analytically determine the probability density function of the embedding angle for Gaussian samples. Maximum‐likelihood decoder is implemented in the receiver side. Owing to embedding in the angle between two line segments, the proposed scheme has high robustness against the gain attacks. In addition, using the low frequency components of the image blocks for data embedding, high robustness against noise and compression attacks has been achieved. Experimental results confirm the validity of the theoretical analyses given in this study and show the superiority of the proposed method against common attacks, such as Gaussian filtering, median filtering and scaling attacks.

A new method is introduced for the least significant bit (LSB) image steganography in spatial domain providing the capacity of one bit per pixel. Compared to the recently proposed image steganography techniques, the new method called one‐third LSB embedding reduces the probability of change per pixel to one‐third without sacrificing the embedding capacity. This improvement results in a better imperceptibility and also higher robustness against well‐known LSB detectors. Bits of the message are carried using a function of three adjacent cover pixels. It is shown that no significant improvement is achieved by increasing the length of the pixel sequence employed. A closed‐form expression for the probability of change per pixel in terms of the number of pixels used in the pixel groups has been derived. Another advantage of the proposed algorithm is to compensate, as much as possible, for any changes in the image histogram. It has been demonstrated that one‐third probability embedding outperforms histogram compensating version of the LSB matching in terms of keeping the image histogram unchanged.

This study presents a robust image watermarking method based on geometric modelling. In this method, nine samples of the approximation coefficient of the image blocks are utilised to construct a plane in the three‐dimensional (3D) space. The authors change the dihedral angle formed between the created plane and the x – y plane for data embedding. To preserve the imperceptibility of the watermark, geometrical computations are used to minimise the embedding distortion. Maximum‐likelihood detector is implemented to extract the watermark in the noisy channel at the receiver side. The authors experimentally determine the probability density function of the embedding dihedral angle for Gaussian samples. Owing to embedding in the dihedral angle between two planes, the proposed scheme has high robustness to gain attacks. In addition, by using the low‐frequency components of the image blocks for data embedding, high robustness against noise and compression attacks has been achieved. Experimental results confirm the validity of the theoretical analysis given in this study and show the superiority of the method over similar techniques in this field. The proposed method is also robust to a wide range of attacks, namely Gaussian filtering, median filtering, JPEG compression, Gaussian noise and scaling.

This paper presents a robust image watermarking scheme based on a sample projection approach. While we consider the human visual system in our watermarking algorithm, we use the low-frequency components of image blocks for data hiding to obtain high robustness against attacks. We use four samples of the approximation coefficients of the image blocks to construct a line segment in the 2-D space. The slope of this line segment, which is invariant to the gain factor, is employed for watermarking purpose. We embed the watermarking code by projecting the line segment on some specific lines according to message bits. To design a maximum likelihood decoder, we compute the distribution of the slope of the embedding line segment for Gaussian samples. The performance of the proposed technique is analytically investigated and verified via several simulations. Experimental results confirm the validity of our model and its high robustness against common attacks in comparison with similar watermarking techniques that are invariant to the gain attack.

Among the many attacks against watermarked data, the gain attack is less supported with countermeasures. The effectiveness of this attack becomes more evident in quantization-based embedding algorithms such as Dither Modulation (DM). In this paper, the general solution for both block and sample type DM schemes that are robust against gain attacks is considered. Among the solutions, we concentrate on a subclass of the algorithms which are insensitive to additive noise attacks; i.e., we introduce watermarking schemes which are both robust against gain and additive noise attacks. The simulation results confirm the desired performance of the final algorithm against these attacks while outperform other gain invariant schemes.

In this paper, an improved multiplicative image watermarking system is presented. Since human visual system is less sensitive to the image edges, watermarking is applied in the contourlet domain, which represents image edges sparsely. In the presented scheme, watermark data is embedded in directional subband with the highest energy. By modeling the contourlet coefficients with General Gaussian Distribution (GGD), the distribution of watermarked noisy coefficients is analytically calculated. The tradeoff between the transparency and robustness of the watermark data is solved in a novel fashion. At the receiver, based on the Maximum Likelihood (ML) decision rule, an optimal detector by the aid of channel side information is proposed. In the next step, a blind extension of the suggested algorithm is presented using the patchwork idea. Experimental results confirm the superiority of the proposed method against common attacks, such as Additive White Gaussian Noise (AWGN), JPEG compression, and rotation attacks, in comparison with the recently proposed techniques.

In this paper, a novel quantization based watermarking method is proposed. For blind detection, a set of nonlinear convex functions based on geometric mean are investigated. In order to achieve minimum distortion, the optimum function set is found. The algorithm is implemented on the approximation coefficients of wavelet transform for natural images. In order to make the algorithm more robust and imperceptible, a new transform domain called Point to Point Graph (PPG), which converts a 1-D signal to a 2-D one, has been used. The error probability of the proposed scheme is analytically investigated. Simulation results show that this algorithm has great robustness against common attacks such as AWGN, JPEG and rotation in comparison with recent methods presented so far.

In this paper, a semi-blind multiplicative watermarking approach for audio and speech signals has been presented. At the receiver end, the optimal maximum likelihood (ML) detector aided by the channel side information for Gaussian and Laplacian signals in noisy environment is designed and implemented. The performance of the proposed scheme is analytically calculated and verified by simulation. Then, we adapt the proposed scheme to speech and audio signals. To improve robustness, the algorithm is applied to low frequency components of the host signal. Besides, the power of the watermark is controlled elegantly to have inaudibility using perceptual evaluation of audio quality (PEAQ) and perceptual evaluation of speech quality (PESQ) algorithms. Experimental results over several audio and speech signals show the higher robustness of the proposed technique in comparison with a recent watermarking scheme.

In this paper, a novel scaling based information hiding approach robust against noise and gain attack is presented. The host signal is assumed to be stationary Gaussian modeled with a first-order autoregressive process. For data embedding, the host signal is divided into two parts. One part is manipulated while the other part is kept unchanged for parameter estimation. The decoding scheme using the ratio of samples is suitable for highly correlated signals in which the decoding process is difficult. By calculating the distribution of the ratio, the performance of the maximum likelihood decoder is analytically studied. The proposed algorithm is applied to several artificial Gaussian autoregressive signals to verify the validity of our results.

In this paper, a new blind scaling based watermarking approach is presented. The host signal is assumed to be stationary Gaussian with first-order autoregressive model. Partitioning the host signal into two separate parts, the data is embedded in one part and the other is kept unchanged for blind parameter estimation. Driving the distribution of the decision variable we have suggested a maximum likelihood decoding algorithm which is independent of the host signal distribution and can be applied for any transform domains. The proposed algorithm is applied to both artificial Gaussian autoregressive signals as well as various test images. Experimental results confirm the independence of the decoder performance to the host signal distribution and its great robustness against common attacks.

This paper presents a Multiplicative Patchwork Method (MPM) for audio watermarking. The watermark signal is embedded by selecting two subsets of the host signal features and modifying one subset multiplicatively regarding the watermark data, whereas another subset is left unchanged. The method is implemented in wavelet domain and approximation coefficients are used to embed data. In order to have an error-free detection, the watermark data is inserted only in the frames where the ratio of the energy of subsets is between two predefined values. Also in order to control the inaudibility of watermark insertion, we use an iterative algorithm to reach a desired quality for the watermarked audio signal. The quality of watermarked signal is evaluated in each iteration using Perceptual Evaluation of Audio Quality (PEAQ) method. The probability of error is also derived for the watermarking scheme and simulation results prove the validity of the analytical derivations. Simulation results show that MPM is robust against various common attacks such as noise addition, filtering, echo, MP3 compression, etc. In comparison to the original patchwork method and its modified versions, and some recent methods, MPM provides more robustness and inaudibility of the watermark insertion.

In this paper, two blind audio watermarking methods using correlated quantization for data embedding with histogram-based detector have been proposed. First, a novel mapping called the point-to-point graph (PPG) is introduced. In this mapping, the value of samples is important as well as the correlation among them. As this mapping increases the dimension of the signal, the data embedding procedure (quantization) will be diversified more securely than that of the 1-D domains such as the time or frequency domains. Hence, two watermarking techniques coined as hard and soft quantization methods based on the quantization of the PPG point radii are suggested. The performance of both techniques is analyzed by obtaining the radii distribution of PPG points after watermarking. Experimental results against AWGN attack confirm the validity of theoretical analysis. Moreover, the robustness of the proposed methods against other common attacks such as echo, low pass, resampling, and MP3 are investigated through extensive simulations.

In this paper, a new scaling-based image-adaptive watermarking system has been presented, which exploits human visual model for adapting the watermark data to local properties of the host image. Its improved robustness is due to embedding in the low-frequency wavelet coefficients and optimal control of its strength factor from HVS point of view. Maximum likelihood (ML) decoder is used aided by the channel side information. The performance of the proposed scheme is analytically calculated and verified by simulation. Experimental results confirm the imperceptibility of the proposed method and its higher robustness against attacks compared to alternative watermarking methods in the literature.

In this paper a new class of invertible watermarking approach based on quantization has been introduced. Based on the necessary conditions (blindness, reversibility and imperceptibility), a set of linear convex functions which satisfy these requirements are found. Then the optimum of this function set with the least distortion has been selected. The main advantage of this method is that the inserted distortion can be easily controlled by adjusting quantization levels. The low computational complexity is another advantage of this method. Experimental results show that the proposed algorithm achieves higher embedding capacity while its distortion is lower than other invertible watermarking techniques.

In this paper, a new multiplicative image watermarking system is presented. As human visual system is less sensitive to the image edges, watermarking is applied in the contourlet domain, which represents image edges sparsely. In the presented scheme, watermark data is embedded in the most energetic directional subband. By modeling general gaussian distribution (GGD) for the contourlet coefficients, the distribution of watermarked noisy coefficients is analytically calculated. At the receiver, based on the maximum likelihood (ML) decision rule, the optimal detector is proposed. Experimental results show the imperceptibility and high robustness of the proposed method against Additive White Gaussian Noise (AWGN) and JPEG compression attacks.