Abstract Absolute Moment Block Truncation Coding (AMBTC) is a cost-effective image compression technique that replaces pixel blocks with two quantization values and a binary map to minimize storage requirements. However, it is less effective for compressing flat and complex blocks. To address this issue, a Dynamic Multi-Grouping (DMG)-based AMBTC method was proposed, which removes the binary map for flat blocks and uses ternary or quaternary maps for complex blocks. Indicators are used to track the encoding methods applied to each block. While this method improves image quality, it fails to account for the correlations among indicators and maps, leading to higher storage requirements. This paper presents a sub-grouping strategy that groups indicators with ternary or quaternary maps to exploit their correlations, and encodes them using entropy coding to reduce the encoding length. During decoding, the compressed codes of indicators can be extracted from the bitstream, and the decompressed indicators are used to reconstruct blocks of various types. Our approach can be integrated with existing DMG methods to reduce bitrate while preserving image quality. When tested on 100 images, it achieved average reductions in bitrate of 31.83% for indicators, 14.68% for ternary maps, and 8.29% for quaternary maps.

Abstract Reversible data hiding (RDH) with contrast enhancement (RDH-CE) is a special type of RDH in improving the subjective visual perception by enhancing the image contrast during the process of data embedding. In RDH-CE, data hiding is achieved via pairwise histogram expansion, and the embedding rate can be increased by performing multiple cycles of histogram expansions. However, when embedding rate gets high, human visible image degradation is observed. Previous work designed an upper bound of the embedding level for RDH-CE, which effectively avoids image over-sharping but offers limited embedding capacity. In this paper, a better tunable bound is designed to enhance the embedding capacity of RDH-CE by exploiting the characteristics of histogram distribution. Furthermore, the objective distortion introduced by histogram pre-shifting is minimized when the embedding level is no more than the upper bound, and the human visible degradation is minimized when the embedding level exceeds the limitation of the proposed upper bound. Experimental results validate that the proposed method provides appropriate upper bound of the embedding level, increases the effective embedding capacity and offers better image contrast.

Outsourcing data mining tasks is beneficial for data owners who either lack expertise in data mining or sufficient computing resources. However, directly releasing the original data would leak private information. Research on Privacy-Preserving Data Mining (PPDM) is dedicated to addressing this issue, the aim of this research is to reduce the risk of privacy violations and preserve the knowledge in the original data. However, most existing methods in the literature ignore the case in which service providers want to verify the integrity and authenticity of their clients’ data to avoid data tampering before performing data mining tasks. In this paper, a new method is proposed to extend the turtle shell algorithm of data hiding to protect the privacy of the original data and to acquire authentication functions simultaneously. The act of data perturbation is performed by replacing data values with their closest neighbors according to a reference matrix. Further, a message authentication code is hidden in the perturbed data to verify the integrity and authenticity of the perturbed data. The experimental results showed that the proposed method achieved the purpose of data perturbation and outperformed similar methods in satisfying the PPDM requirement.

Recently, a number of high-fidelity reversible data hiding algorithms have been developed based on prediction-error expansion (PEE) and pixel sorting. In PEE, prediction is made using either a full-enclosed or a half-enclosed predictor. While in PEE with pixel sorting, the local complexity (LC), which is usually assumed to be proportional to the magnitude of prediction-error (PE), is exploited to reduce the embedding distortion. However, this assumption may not always hold in all conditions. In this letter, a directional enclosed predictor is proposed to detect the locations where LC is not proportional to PE. And, a directionally enclosed prediction and expansion (DEPE) scheme is then developed for efficient reversible data hiding. With DEPE, data embedding is restricted to pixels where LC correlates to PE with a proportional relationship. Experimental results show that, compared to the full-enclosed or half-enclosed prediction schemes, DEPE significantly improves the image fidelity while providing a considerable payload.

Recently, Luo et al. proposed a new block-based reversible data hiding method that divides an image into blocks, sorts all pixels within a block and then chooses the middle sorted pixel as a basic-point. To maintain the same basic-point before and after embedding, Luo et al.'s method classifies blocks into four types and adopts four different strategies, respectively, to embed a secret message. In this paper, we propose a new method which relies on only one strategy to reach the same effectiveness. The proposed method includes a complexity controlled mechanism to filter blocks that are not suitable for hiding the secret message. High image quality is thereby retained. Based on just-noticeable difference mechanism, the proposed scheme also estimates allowable changes such that the stego-image is able to meet the human visual system. Experimental results showed that the proposed method allows more payload and higher image quality than the method of Luo et al. and other related methods.

This letter proposes an improved version of Zhang's reversible data hiding method in encrypted images. The original work partitions an encrypted image into blocks, and each block carries one bit by flipping three LSBs of a set of pre-defined pixels. The data extraction and image recovery can be achieved by examining the block smoothness. Zhang's work did not fully exploit the pixels in calculating the smoothness of each block and did not consider the pixel correlations in the border of neighboring blocks. These two issues could reduce the correctness of data extraction. This letter adopts a better scheme for measuring the smoothness of blocks, and uses the side-match scheme to further decrease the error rate of extracted-bits. The experimental results reveal that the proposed method offers better performance over Zhang's work. For example, when the block size is set to 8 8, the error rate of the Lena image of the proposed method is 0. 34%, which is significantly lower than 1.21% of Zhang's work.

This paper proposes a new data-hiding method based on pixel pair matching (PPM). The basic idea of PPM is to use the values of pixel pair as a reference coordinate, and search a coordinate in the neighborhood set of this pixel pair according to a given message digit. The pixel pair is then replaced by the searched coordinate to conceal the digit. Exploiting modification direction (EMD) and diamond encoding (DE) are two data-hiding methods proposed recently based on PPM. The maximum capacity of EMD is 1.161 bpp and DE extends the payload of EMD by embedding digits in a larger notational system. The proposed method offers lower distortion than DE by providing more compact neighborhood sets and allowing embedded digits in any notational system. Compared with the optimal pixel adjustment process (OPAP) method, the proposed method always has lower distortion for various payloads. Experimental results reveal that the proposed method not only provides better performance than those of OPAP and DE, but also is secure under the detection of some well-known steganalysis techniques.

Recently, a new personalized model has been developed to prevent credit card fraud. This model is promising; however, there remains some problems. Existing approaches cannot identify well credit card frauds from few data with skewed distributions. This paper proposes to address the problem using a binary support vector system (BSVS). The proposed BSVS is based on the support vectors in the support vector machines (SVM) and the genetic algorithm (GA) is employed to select support vectors. To obtain a high true negative rate, self-organizing mapping (SOM) is first employed to estimate the distribution model of the input data. Then BSVS is used to best train the data according to the input data distribution to obtain a high detection rate. Experimental results show that the proposed BSVS is effective especially for predicting a high true negative rate.

For a biologist, accurate protein information on 2DGE images is very important for making decisive diagnostics. The proposed scheme in this paper is to combine a local threshold technique with the ACM technique to enhance detections for protein spots. First, the 2DGE image is normalized with thresholds calculated using Otsu's algorithm. The normalized image is then transformed into a black-and-white local threshold image where the ACM technique is applied to increase detections for protein spots. With this technique, more lighter-colored protein spots can be detected.

Based on discrete wavelet transformation and block truncation coding, in this paper, a system is presented to cope with a tampered digital image. Without contrast with the original image, this system has its great advantage to detect any tampered possibility, and then restore that tampered part. Experiments have indicated that restored version is extremely close to the original one.

Based on a visual cryptography scheme, an effective and generalized scheme of color image hiding is proposed. By means of little additional computations, it goes through a color index table to hide and recover a secret image. In this scheme, a secret color image hides itself in two arbitrary color images, which can be constructed and then are kept by two participants, separately. Note that the processed images are significant. It is a kind of camouflage. Later, the secret image can be recovered through the same simple algorithms on two camouflage color images. The paper proves that only one of the two camouflage color images cannot reveal the secret image. Thus, the hidden information is secured.

For fast vector quantization (VQ) encoding, we present in this paper a new method to speed up the calculation of the squared Euclidean distance between two vectors. We call it the approximated look-up table (ALUT) method. This method considers the frequency of each squared number that occurs in the equation of squared Euclidean distances, and generates a more practical table to store squared numbers. ALUT makes use of this table and some simple operations to speed up the calculation of squared Euclidean distances. From the VQ simulation results, we see that ALUT saves memory and produces better image quality compared with some other methods. It is a suitable method for VLSI implementation.

We propose a new image cryptosystem to protect image data. It encrypts the original image into another virtual image. Since both original and virtual images are significant, our new cryptosystem can confuse illegal users. Besides the camouflage, this new cryptosystem has three other benefits. First, our cryptosystem is secure even if the illegal users know that our virtual image is a camouflage. Second, this cryptosystem can compress image data. Finally, our method is more efficient than a method that encrypts the entire image directly.

Vector quantization (VQ) is a fundamental technique for image compression. But it takes time to search for a similar codeword in a codebook. Thus, the codebook search is one of the major bottlenecks in VQ. We propose a new search algorithm which is used to speed up both the codebook generation and the encoding. We call it the diagonal axes method (DAM). This new algorithm contains two major techniques: diagonal axes analysis (DAA) and orthogonal checking (OC). Since most of these procedures simply involve additions and subtractions, DAM is more efficient than some other related algorithms. Simulation results confirm this effectiveness.

A new side-match vector quantizer, NewSMVQ, is presented in this paper. Three techniques are incorporated to improve the image quality, encoding speed, and bit rate for compressing images. The experimental result shows: i) the encoding time of NewSMVQ is almost 7 times faster than that of SMVQ (ordinary fixed-rate side-match vector quantizer) and CSMVQ (variable-rate SMVQ) and ii) NewSMVQ outperforms SMVQ and CSMVQ in terms of bit rate versus image quality tradeoffs.