Abstract Pattern substitution (PS) method (Ho et al., Comput. Stand. Interfaces 31:787–794, 2009) is a recent reversible data hiding method for binary images. It generates one pattern pair, the patterns in which are called PM and PF, and substitutes between them to embed one bit. Two types of PS have been proposed: non-overlapping PS and overlapping PS. However, Dong et al. (ETRI J. 37:990–1000, 2015) states that the overlapping PS sometimes cannot decode correctly, which is called as miscoding problem. Moreover, to the PS-based methods (Ho et al., Comput. Stand. Interfaces 31:787–794, 2009; Dong et al., ETRI J. 37:990–1000, 2015; Dong et al., Lect. Notes Comput. Sci. 6526:3516–3522, 2011), although chosen of two patterns to form the pattern pair which relates the embedding performance much, the previous methods only use a fixed pattern pair during the whole data hiding process. In order to overcome these two shortcomings, this paper proposes an adaptive overlapping PS method. Firstly, two modes of the embedding are proposed so that the miscoding problem can be solved. Secondly, one window which contains the context of current processing pixel is generated and updated when the process proceeds to the next pixel. Thus, the optimum pair of the local context for embedding can be chosen. Thirdly, in order to get a smaller location map, a three-round embedding mechanism is also proposed. With these three improvements, our method can embed larger payloads with less distortion than the previous works.

Reversible data hiding with automatic contrast enhancement methods provide an interoperable way to reduce the storage requirement for automatic image enhancement applications: original image can be recovered from the enhanced image without any additional information. Unlike the previous work, where the goal was to maximize the contrast, the proposed method increases the contrast to an appropriate level using an idea called brightness preservation. This is achieved by using an adaptive bin selection process based on the original brightness. Extensive experimental results verify that the enhanced images produced using the proposed method are visually and quantitatively superior than the existing work.

Reversible data hiding hides data in an image such that the original image is recoverable. This paper presents a novel embedding framework with reduced distortion called skewed histogram shifting using a pair of extreme predictions. Unlike traditional prediction error histogram shifting schemes, where only one good prediction is used to generate a prediction error histogram, the proposed scheme uses a pair of extreme predictions to generate two skewed histograms. By exploiting the structure of the skewed histogram, only the pixels from the peak and the short tail are used for embedding, which decreases the distortion from the lesser number of pixels being shifted. Detailed experiments and analysis are provided using several image databases.

Among various digital image formats used in daily life, the Joint Photographic Experts Group (JPEG) is the most popular. Therefore, reversible data hiding (RDH) in JPEG images is important and useful for many applications such as archive management and image authentication. However, RDH in JPEG images is considerably more difficult than that in uncompressed images because there is less information redundancy in JPEG images than that in uncompressed images, and any modification in the compressed domain may introduce more distortion in the host image. Furthermore, along with the embedding capacity and fidelity (visual quality), which have to be considered for uncompressed images, the storage size of the marked JPEG file should be considered. In this paper, based on the philosophy behind the JPEG encoder and the statistical properties of discrete cosine transform (DCT) coefficients, we present some basic insights into how to select quantized DCT coefficients for RDH. Then, a new histogram shifting-based RDH scheme for JPEG images is proposed, in which the zero coefficients remain unchanged and only coefficients with values 1 and -1 are expanded to carry message bits. Moreover, a block selection strategy based on the number of zero coefficients in each 8 × 8 block is proposed, which can be utilized to adaptively choose DCT coefficients for data hiding. Experimental results demonstrate that by using the proposed method we can easily realize high embedding capacity and good visual quality. The storage size of the host JPEG file can also be well preserved.

Lately, electroencephalography (EEG)-based auth- entication has received considerable attention from the scientific community. However, the limited usability of wet EEG electrodes as well as low accuracy for large numbers of users have so far prevented this new technology to become commonplace. In this study a novel EEG-based authentication system is presented, which is based on the rapid serial visual presentation paradigm and uses a knowledge-based approach for authentication. Twenty-nine subjects’ data were recorded and analyzed with wet EEG electrodes as well as dry ones. A true acceptance rate of 100% can be reached for all subjects with an average required login time of 13.5 s for wet and 27 s for dry electrodes. Average false acceptance rates for the dry electrode setup were estimated to be$3.33 \times 10^{-5}$.

This paper presents a novel weighted sparse representation classification for face recognition with a learned distance metric (WSRC-LDM) which learns a Mahalanobis distance to calculate the weight and code the testing face. The Mahalanobis distance is learned by using the information-theoretic metric learning (ITML) which helps to define a better weight used in WSRC. In the meantime, the learned distance metric takes advantage of the classification rule of SRC which helps the proposed method classify more accurately. Extensive experiments verify the effectiveness of the proposed method.

In this paper, a new class of image texture operators is proposed. We firstly determine that the number of gray levels in each B × B subblock is a fundamental property of the local image texture. Thus, an occurrence histogram for each B × B sub-block can be utilized to describe the texture of the image. Moreover, using a new multi-bit plane strategy, i.e., representing the image texture with the occurrence histogram of the first one or more significant bit-planes of the input image, more powerful operators for describing the image texture can be obtained. The proposed approach is invariant to gray scale variations since the operators are, by definition, invariant under any monotonic transformation of the gray scale, and robust to rotation. They can also be used as supplementary operators to local binary patterns (LBP) to improve their capability to resist illuminance variation, surface transformations, etc.

This paper presents an improved data hiding technique based on BCH (n,k,t) coding. The proposed embedder hides data into a block of input data by modifying some coefficients in the block in order to null the syndrome. The proposed embedder can hide data with less computational time and less storage capacity compared to the existing methods. The complexity of the proposed method is linear while that of other methods are exponential for any block sizen. Thus, it is easy to extend this method to a largen. The BCH syndrome coding for steganography is now viable ascribed to the reduced complexity and its simplicity of the proposed embedder.

This paper advances a new lossless data compression method utilizing reversible data hiding to reduce compression times significantly and achieve good compression rates. The core idea of the new approach is to transform a longer binary sequence into a shorter n-ary sequence, transform the n-ary sequence into two binary subsequences, and apply a lossless coding algorithm based on the assumption that a shorter sequence obviously takes less time than a longer sequence. Mathematical conditions to show when the theory proposed in this paper is attainable are provided. The proposed method keeps as good of a compression rate as arithmetic coding, but takes much less time than that. Experiments show that the proposed theory is true.

In this paper, we present a steganographic method that improves some existing data hiding methods. This method works in compressed domain (JPEG) of image. In most of the previous methods, they usually change coefficients to hide information. And some methods tried to enhance the performance of general steganographic method like F5 including matrix encoding. In above papers, they achieve to reduce the distortion after hiding data into coefficient of compressed domain. We have been analyzed the effect of quantization table during data hiding on compressed domain. Finally, we considered that there would be more chance to reduce the distortion of steganographic method. As the result, this paper provides little bit more improved result in view of image quality. This result can give some contribution for future steganographic methods which will work in compressed domain like JPEG.

This paper presents a new lossless data compression method which can be used for the next-generation image and video codec. As an alternative system for CABAC (context adaptive binary arithmetic coder), a new binary coding algorithm is introduced. However, this method can be applicable to any kind of symbols. Efficiency of the codec in terms of compression ratio and computational complexity is introduced. Basic idea behind the proposed method is hiding data reversibly with a location map.

This paper presents a reversible or lossless watermarking algorithm for images without using a location map in most cases. This algorithm employs prediction errors to embed data into an image. A sorting technique is used to record the prediction errors based on magnitude of its local variance. Using sorted prediction errors and, if needed, though rarely, a reduced size location map allows us to embed more data into the image with less distortion. The performance of the proposed reversible watermarking scheme is evaluated using different images and compared with four methods: those of Kamstra and Heijmans, Thodi and Rodriguez, and Lee et al. The results clearly indicate that the proposed scheme can embed more data with less distortion.

Watermark resistance to geometric attacks is an important issue in the image watermarking community. Most countermeasures proposed in the literature usually focus on the problem of global affine transforms such as rotation, scaling and translation (RST), but few are resistant to challenging cropping and random bending attacks (RBAs). The main reason is that in the existing watermarking algorithms, those exploited robust features are more or less related to the pixel position. In this paper, we present an image watermarking scheme by the use of two statistical features (the histogram shape and the mean) in the Gaussian filtered low-frequency component of images. The two features are: 1) mathematically invariant to scaling the size of images; 2) independent of the pixel position in the image plane; 3)statistically resistant to cropping; and 4) robust to interpolation errors during geometric transformations, and common image processing operations. As a result, the watermarking system provides a satisfactory performance for those content-preserving geometric deformations and image processing operations, including JPEG compression, lowpass filtering, cropping and RBAs.

Reversible data embedding theory has marked a new epoch for data hiding and information security. Being reversible, the original data and the embedded data should be completely restored. Difference expansion transform is a remarkable breakthrough in reversible data-hiding schemes. The difference expansion method achieves high embedding capacity and keeps distortion low. This paper shows that the difference expansion method with the simplified location map and new expandability can achieve more embedding capacity while keeping the distortion at the same level as the original expansion method. Performance of the proposed scheme in this paper is shown to be better than the original difference expansion scheme by Tian and its improved version by Kamstra and Heijmans. This improvement can be possible by exploiting the quasi-Laplace distribution of the difference values.

Conventional divisible load scheduling algorithms attempt to achieve optimal partitioning of massive loads to be distributed among processors in a distributed computing system in the presence of communication delays in the network. However, these algorithms depend strongly upon the assumption of prior knowledge of network parameters and cannot handle variations or lack of information about these parameters. In this paper, we present an adaptive strategy that estimates network parameter values using a probing technique and use them to obtain optimal load partitioning. Three algorithms, based on the same strategy, are presented in the paper, incorporating the ability to cope with unknown network parameters. Several illustrative numerical examples are given. Finally, we implement the adaptive algorithms on an actual network of processor nodes using MPI implementation and demonstrate the feasibility of the adaptive approach.

The paper presents the modified patchwork algorithm (MPA), a statistical technique for an audio watermarking algorithm in the transform (not only discrete cosine transform (DCT), but also DFT and DWT) domain. The MPA is an enhanced version of the conventional patchwork algorithm. The MPA is sufficiently robust to withstand some attacks defined by the Secure Digital Music Initiative (SDMI). Experimental results show that the proposed watermarking algorithm is sustainable against compression algorithms such as MP3 and AAC, as well as common signal processing manipulation attacks.

The paper presents a novel echo-hiding method for audio watermarking. The method is quite different from previous echo-hiding methods since it presents a new echo kernel which introduces a forward kernel as well as a backward kernel. The new kernel, a combination of the backward and forward kernels, can enhance considerably the watermark detection rate. Thus, it is possible to reduce echo amplitude. The paper proves mathematically that the combination of kernels improves watermark performance. Experimental results show that the proposed watermarking scheme is much better than previous echo-hiding schemes in terms of detection rate and imperceptibility.

Scalable data parallel algorithms for geometric hashing are presented. Implementations of the proposed algorithms are performed on MasPar MP-1/MP-2. New parallel algorithms are designed and mapped onto MP-1/MP-2. These techniques significantly improve upon the number of processors employed while achieving superior time performance. The authors' implementations run on a P processor machine, such that 1/spl les/P/spl les/S, where S is the number of feature points in the scene. The results show that a probe of the recognition phase for a scene consisting of 1024 feature points takes less than 50 ms on a 1-K processor MP-1/MP-2.>