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Contrast enhancement (CE) of magnetic resonance (MR) brain images is an important technique to bring out the tissue details for clinical diagnosis. Recently, a new form of image enhancement has been proposed to complete the task without any information loss. Specifically, information required to restore the original image is reversibly hidden into the enhanced image. Moreover, several image segmentation based algorithms have been proposed so that the region of interest can be exclusively enhanced. However, with the reversible algorithms, it is hard to properly enhance the tissues in MR brain images when they are relatively small or connected with each other. To address this issue, a hierarchical CE scheme is proposed for MR brain images with reversibility in this letter. Firstly, a deep convolutional neural network is used to segment multiple tissue classes automatically. Then, the segmented tissues are individually utilized to guide the CE procedure so that individual-tissue-enhanced images are generated. Compared with using the background information to guide the CE procedure, better tissue enhancement effects and visual quality are both obtained by our proposed hierarchical scheme. The evaluation results obtained over MR brain test images demonstrate the reversibility and adaptability of the proposed scheme for the enhancement of interested tissues.

In this paper, a novel image transformation scheme is proposed to protect the visual information. By mimicking an arbitrarily chosen reference image, a secret image is visually changed and can be exactly recovered from the transformed image when needed. Unlike the block-wise visual encryption methods, the proposed transformation scheme modifies the secret image by bit plane replacement and reordering so that no block effect is introduced. In particular, one or more bit planes are hidden into the other bit planes so that the most significant one(s) can be vacated. Besides replacing the vacated bit plane(s) and reordering the others according to the reference image, histogram modification may be performed to conceal the secret content if needed. To exactly recover the secret image, the required information is recorded and reversibly hidden into the transformed image by adopting a reversible data hiding algorithm. The experimental results on three image sets show that their content can be semantically changed to prevent leakage of visual information. Moreover, the applicability and efficiency of the proposed scheme have been validated by comparing the existing visual encryption schemes.

Contrast enhancement (CE) of medical images is helpful to bring out the unclear content in the interested regions. Recently, reversible CE has been proposed so that the original version of a contrast-changed image can be exactly recovered. This property can be used to save storage space or facilitate the archiving system. To enhance the regions of interest (ROI) without introducing visual distortions, the technique of image segmentation (e.g. using Otsu's method) has been used to obtain the background before conducting the CE process. To segment the ROI more accurately, an interactive algorithm called GrabCut is employed in the proposed scheme. In addition, a new preprocessing strategy is adopted to preserve the image quality through the CE process. Consequently, the content in the selected regions can be better brought out while the reversibility of the CE process is achieved. The experimental results on 30 chest radiograph images and 20 magnetic resonance images have demonstrated the efficacy of the proposed scheme for reversible CE. The evaluation results are provided to show the better performances of the proposed method in achieving CE effects and preserving image quality.

Recently, reversible data hiding in encrypted images has been developed to transmit useful data while the original images can be perfectly recovered when needed. In this paper, a new method is proposed for homomorphic encrypted images so that part of the hidden data can be extracted in encrypted domain and the rest are extractable after image decryption. Specifically, a plain-text image is preprocessed by reversibly embedding the bit values of some pixels into the image. The preprocessed image is encrypted in Paillier cryptosystem and two embedding algorithms are applied on the encrypted image in succession. Compared with the state-of-the-art schemes, higher embedding capacity can be achieved by applying the proposed method, respectively for data extraction before and after image decryption. Compared with the schemes with similar properties, better performances are achieved with the proposed method in terms of quality of directly decrypted image with respect to data hiding rate.

With the widespread adoption of Internet of Things (IoT) and cloud computing, more and more individuals and organizations are outsourcing their IoT data in the cloud server. Under these circumstances, how to protect the privacy and security of the outsourced data becomes a key challenge. Public key encryption with keyword search (PEKS) is a promising technology to solve the problem. In this paper, we present a new kind of PEKS named time controlled public key encryption with delegated conjunctive keyword search (tc-PEDCKS) for IoT deployment. A basic tc-PEDCKS is firstly presented which supports conjunctive keyword search and enables a data user to delegate access right over the data to others in a limited time period. To resist against the keyword guessing attacks, a more secure tc-PEDCKS with designated server construction is also presented. The two constructions are proved secure in the random oracle model. The performance evaluation and experimental results demonstrate that our constructions are practical and feasible for real world IoT scenarios.

The rapid proliferation of mobile sensing devices has promoted the emergence of a novel sensing paradigm mobile crowdsensing (MCS) for Internet of Things (IoT). For the data sensing in various mobile devices carried by different users, the sensed data may contain a variety of noise and sensitive information of mobile users. In order to extract the optimal truth from the noisy sensing data without privacy breach, privacy-preserving truth discovery (PPTD) has been proposed recently. However, there are two limitations to apply the existing PPTD approaches in real-time MCS applications. First, it is often ignored that the correctness of estimated truths may be susceptible to the dropout of mobile devices. Second, large-scale PPTD MCS application cannot be deployed, especially in real-time scenarios, due to the inefficiency of heavy cryptographic operations and iterative truth discovery algorithm. In this paper, we design and implement a real-time PPTD framework for crowdsensed data streams. Our design is based on the incremental conflict resolution on heterogeneous data, which is very efficient in processing data streams. In this framework, a low-overhead secure summation aggregation protocol is customized between an untrusted aggregator and a sensing client to estimate the true value of the sensed object. In addition, it should be noted that our system only needs one service provider. Theoretical analysis demonstrates that our design accomplishes both privacy-preserving and failure-robust. Through the complexity analysis and concrete implementation, the results show that the framework can achieve better scalability.

Recently, reversible image data hiding with contrast enhancement (CE) has been proposed so that a contrast-changed image can be converted to its original version when needed. Several reversible image CE methods have been proposed by adopting the technique of reversible data hiding (RDH) to embed the recovery information into the contrast-enhanced images. In these methods, the 1D histogram is calculated and then modified to achieve the effects of histogram equalization (HE). Meanwhile, the 2D histogram has been used in image CE and demonstrated remarkable advantages in improving the image quality. In this paper, the method based on the 2D histogram modification is proposed to perform image CE and RDH simultaneously. In particular, a preprocessing strategy is developed to merge the adjacent bins in 2D histogram to prevent the overflow and underflow of the pixel values due to HE. The changes made in preprocessing is minimized by choosing the lowest bins for merging, while the CE effects can be achieved by expanding the highest bins for data embedding. The experimental results on two sets of test images have clearly demonstrated the efficacy of the proposed method for reversible image CE. The evaluation results are given to show its superior performances in CE and image quality preservation by comparing with the state-of-the-art approaches.

With the popularity of cloud computing and social networks, more and more JPEG images are stored and distributed. Consequently, how to protect privacy and content in JPEG images has become an important issue. Although traditional encryption schemes can be employed, the file format of JPEG images is changed so that their usage may be affected. In this paper, a reversible visual transformation algorithm is proposed to protect content in JPEG images. Specifically, the DC coefficient in each user-selected block is modified, while the information required to recover it is reversibly hidden into AC coefficients. Then the signs of AC coefficients in the selected blocks are flipped and the blocks are further scrambled with a secret key. By embedding the location information of the selected blocks in a transformed image, the original image can be exactly recovered when needed. Besides, regions to be protected can be arbitrarily chosen without substantially affecting the rest of the image. The experimental results on a set of JPEG images validate the efficacy and reversibility of the proposed algorithm. In addition, good performance is achieved in terms of invisibility of the protected content, image quality, file size preservation and security.

Recently, reversible contrast enhancement (CE) is achieved so that an original image can be recovered after its contrast has been changed. As most of the reversible CE methods are primarily proposed for gray-level images, directly applying them to color images may cause visual distortions. To cope with this issue, a new reversible CE scheme is proposed based on the HSV (hue, saturation, and value) color model. In particular, hue component is preserved during histogram equalization of value component and a new preprocessing is designed for original RGB (red, green, and blue) images. The experimental results show that exact recovery of all test images can be achieved with the proposed scheme. Moreover, several metrics are adopted to measure CE effects, image quality and color difference, respectively. Compared with separately conducting the CE process on the RGB channels, less color differences and better image quality can be simultaneously obtained with the proposed scheme.

Recently, reversible data hiding in encrypted images (RDH-EI) has been developed to transmit additional data. Besides extracting the hidden data, the original or processed image should be obtained when needed. In this paper, a new RDH-EI method for homomorphic encrypted images is proposed by utilizing the additive homomorphism and self-blinding property in Paillier cryptosystem. Specifically, part of the hidden data may be extracted before image decryption while the rest can be extracted after image decryption. In addition, no preprocessing is required so that homomorphic processing can be performed before data embedding. The experimental results on test images validate that the proposed method is compatible with homomorphic image processing before data embedding. Compared with the state-of-the-art methods, higher embedding capacity can be obtained with the proposed method while the original or processed image can be correctly generated.

Recently, several image contrast enhancement methods have been proposed such that the original image can be recovered from its contrast-enhanced version. Hence a flexibility in changing image contrast can be provided when needed. However, the artificial distortions may be introduced into the image content after adopting these methods. Meanwhile, there is lack of using the adequate image quality metrics for performance evaluation. In this paper, a novel reversible data hiding method is proposed for image contrast enhancement. To better preserve image quality, it is restricted that only the adjacent bins in the original image histogram may be merged in the pre-processing. The proposed method has been applied to two image sets and compared with the previous methods. For image quality assessment, the PSNR, SSIM and three no-reference metrics have been adopted in performance evaluation. The experimental results have clearly shown that better visual quality can be achieved with the proposed method. Besides recovering the original images, extra data can be hidden into the contrast-enhanced images and correctly extracted.

We present a novel method to design a conventional Fabry–Perot (F-P) antenna but with low scattering. Combining a coding metasurface and an F-P antenna together could effectively reduce the scattering and keep high gain simultaneously. The coding element consists of two layers of square metallic patches printed on both sides of a dielectric substrate. The bottom metallic patch helps form a partially reflecting surface (PRS) for the F-P antenna, while the upper metallic patch is utilized to construct the coding metasurface with an optimized coding sequence, aiming to reduce the scattering of the F-P antenna by redirecting electromagnetic energies in all directions. Based on the specially designed coding metasurface, a good scattering reduction without degrading the radiation performance of the F-P antenna is achieved. Both simulated and experimental results demonstrate the excellent performance of the proposed antenna, with a peak measured gain of 19.8 dBi and a significant scattering reduction in the frequency range of 8–12 GHz.

Recently, lossless contrast enhancement (CE) has been proposed so that a contrast-changed image can be converted to its original version by maintaining information entropy in it. As most of the lossless CE methods are proposed for grayscale images, artifacts are probably introduced after directly applying them to color images. For instance, color distortions may be caused after CE is separately conducted in each channel of the RGB (red, green, and blue) model. To cope with this issue, a new scheme is proposed based on the HSV (hue, saturation, and value) color model. Specifically, both hue and saturation components are kept unchanged while only the value component is modified. More precisely, the ratios between the RGB components are maintained while a reversible data hiding method is applied to the value component to achieve CE effects. The experimental results clearly show CE effects obtained with the proposed scheme, while the original color images can be perfectly recovered. Several metrics including image entropy were adopted to measure the changes made in CE procedure, while the performances were compared with those of one existing scheme. The evaluation results demonstrate that better image quality and increased information entropy can be simultaneously achieved with our proposed scheme.

Providing appropriate monetary incentives for participants is vital for crowdsensing to encourage their participation. Among all outstanding incentive mechanisms, posted pricing has been widely adopted because it is easy to implement and naturally achieves truthfulness and fairness. However, existing schemes either lack of privacy protection for the sensing data of participants, or fail to consider the diversity of sensing quality in crowdsensing systems. To address these critical problems, we propose a privacy-preserving dynamic pricing mechanism for robust crowdsensing, which only needs to spend a small amount of total payments to recruit a group of mobile users with reasonable sensing quality while protecting the sensing data privacy of each participant. Specifically, we first design an efficient secure aggregation algorithm through which the platform can compute the sum of sensing data from participants without learning each participant’s individual data. Then, we employ the aggregation algorithm to design a secure quality assessment algorithm to obtain the sensing quality levels of participants. Finally, according to the varying quality levels, we develop a model-free reinforcement learning based approach to optimize pricing policy to achieve lower total payments and robustness requirement. Through privacy analysis and extensive experiments, we demonstrate the effectiveness and efficiency of the proposed mechanism.

Reversible image data hiding in the Paillier cryptosystem has been introduced.Two new reversible data hiding algorithms are proposed for the encrypted images.The potential applications of the proposed algorithms have been discussed.High-capacity reversible data hiding can be achieved in the Paillier cryptosystem. In this paper, reversible image data hiding in the Paillier cryptosystem is investigated. To transmit additional data in homomorphic encryption domain, two algorithms are proposed for different application scenarios. By exploiting the additive homomorphism, high-capacity data hiding can be accomplished with the first algorithm by conducting value expansion on the encrypted pixel values. But the hidden data can only be extracted after image decryption (i.e., in plain-text domain). With the second algorithm, both of data embedding and extraction can be performed in the encryption domain by exploiting the self-blinding property, while the corresponding plain-text values are unchanged. Compared with the reversible data hiding algorithms designed for encryption with a cipher stream, the proposed ones are more applicable in cloud computing without degrading the security level. Thus the additional data can be losslessly transmitted in the different applications of Paillier cryptosystem.

In the past two decades, reversible data hiding (RDH), also referred to as lossless or invertible data hiding, has gradually become a very active research area in the field of data hiding. This has been verified by more and more papers on increasingly wide-spread subjects in the field of RDH research that have been published these days. In this paper, the various RDH algorithms and researches have been classified into the following six categories: 1) RDH into image spatial domain; 2) RDH into image compressed domain (e.g., JPEG); 3) RDH suitable for image semi-fragile authentication; 4) RDH with image contrast enhancement; 5) RDH into encrypted images, which is expected to have wide application in the cloud computation; and 6) RDH into video and into audio. For each of these six categories, the history of technical developments, the current state of the arts, and the possible future researches are presented and discussed. It is expected that the RDH technology and its applications in the real word will continue to move ahead.

JPEG images are widely used in daily life. Reversible data hiding (RDH) in JPEG images is useful for many applications. How to avoid image distortion and preserve file size is very important for RDH in JPEG images. In this paper, a novel RDH scheme is proposed for JPEG images based on improved block ordering and frequency selection. The distortion that may be caused by the data embedding in each block is estimated more accurately based on the DCT coefficient distribution, and the blocks that result in less distortion will be firstly used for data embedding. Moreover, the non-zero AC coefficients with the frequencies having higher embedding efficiency will be used preferentially to further reduce image distortion. Consequently, less side information on frequency selection is needed for data extraction and image recovery with our scheme. Extensive experiments are conducted on several typical images and 500 images randomly selected from BOSSbase image database, and the numerical results demonstrate that better performance in terms of visual quality and file size preservation can be achieved with the proposed scheme compared to the state-of-the-art RDH schemes for JPEG images.

An image reversible transformation method is proposed for secret image transmission and privacy protection. With the proposed method, the visual content of a secret image is changed to mimic a target image while the secret image can be recovered from the transformed image. Different from the previous methods that divide the secret image into blocks, our method processes the secret image as a whole so that no block artifact will be caused. Firstly, the most significant bit (MSB) plane of the secret image is replaced and then a process called histogram bin mapping is conducted. The replaced MSB plane and histogram bin mapping information are further hidden into the transformed image. Via reversible data hiding, the secret image can be exactly recovered by extracting the hidden data and reversing the transformation process. The experimental results show that the visual content of secret images can be semantically changed, while almost all of the test images were successfully recovered from the corresponding transformed images.

In this paper, image quality assessment (IQA) in reversible data hiding with contrast enhancement (RDH-CE) is studied. Firstly, the schemes of RDH-CE are reviewed, with which image contrast can be enhanced without any information loss. Secondly, the limitations of using the peak signal-to-noise ratio (PSNR) to indicate image quality in the scenario of RDH-CE are discussed. Subsequently, three no-reference IQA metrics and four metrics specially designed for contrast-changed images are adopted, in addition to PSNR and structural similarity (SSIM) index. By using these metrics, the evaluation results on the contrast-enhanced images generated with two RDH-CE schemes are obtained and compared. The experimental results have shown that the no-reference IQA metrics, the blind/referenceless image spatial quality evaluator (BRISQUE) for instance, are more suitable than PSNR and SSIM index for the images that have been enhanced by the RDH-CE schemes. Furthermore, how to use the suitable IQA metrics has been discussed for performance evaluation of RDH-CE schemes.