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.

Reversible Data Hiding: Advances in the Past Two Decades

Integrated chaotic systems for image encryption

Reversible data hiding in encrypted images has become an effective and popular way to preserve the security and privacy of users’ personal images. Recently, Xiao et al. firstly presented reversible data hiding in encrypted images with use of the modern signal processing technique compressive sensing (CS). However, the quality of decrypted image is not great enough. In this paper, a new method of separable data hiding in encrypted images are proposed by using CS and discrete fourier transform, which takes full advantage of both real and imaginary coefficients for ensuring great recovery and providing flexible payload. Compared with the original work, the proposed method can obtain better image quality when concealing the same embedding capacity. Furthermore, image decryption and data extraction are separable in the proposed method, and the secret data can be extracted relatively accurately.

Separable data hiding in encrypted image based on compressive sensing and discrete fourier transform

Unlike the visual object tracking, thermal infrared object tracking can track a target object in total darkness. Therefore, it has broad applications, such as in rescue and video surveillance at night. However, there are few studies in this field mainly because thermal infrared images have several unwanted attributes, which make it difficult to obtain the discriminative features of the target. Considering the powerful representational ability of convolutional neural networks and their successful application in visual tracking, we transfer the pre-trained convolutional neural networks based on visible images to thermal infrared tracking. We observe that the features from the fully-connected layer are not suitable for thermal infrared tracking due to the lack of spatial information of the target, while the features from the convolution layers are. Besides, the features from a single convolution layer are not robust to various challenges. Based on this observation, we propose a correlation filter based ensemble tracker with multi-layer convolutional features for thermal infrared tracking (MCFTS). Firstly, we use pre-trained convolutional neural networks to extract the features of the multiple convolution layers of the thermal infrared target. Then, a correlation filter is used to construct multiple weak trackers with the corresponding convolution layer features. These weak trackers give the response maps of the target’s location. Finally, we propose an ensemble method that coalesces these response maps to get a stronger one. Furthermore, a simple but effective scale estimation strategy is exploited to boost the tracking accuracy. To evaluate the performance of the proposed tracker, we carry out experiments on two thermal infrared tracking benchmarks: VOT-TIR 2015 and VOT-TIR 2016. The experimental results demonstrate that our tracker is effective and achieves promising performance.

Deep convolutional neural networks for thermal infrared object tracking

Due to the interesting nonlinear dynamic properties of chaotic maps, recently chaos-based encryption algorithms have gained much attention in cryptographic communities. However, many encryption schemes do not fulfil the minimum key space requirement, which is an essential concern in many secure data applications. In this paper, an efficient chaos-based image encryption scheme with higher key space is presented. Even with a single round of encryption, a significantly larger key space can be achieved. The proposed scheme removes correlation among image pixels via random chaotic sequences, simply by XOR and addition operations. In order to resist against numerous attacks, we apply the affine transformation to get the final ciphertext image. The security of the proposed scheme is proved through histogram, contrast, PSNR, entropy, correlation, key space, key sensitivity and differential attack analysis. Many significant properties of chaotic maps, sensitivity to initial condition and control parameters, structure and attack complexity, make the anticipated scheme very reliable, practical and robust in various secure communication applications.

A secure image encryption scheme based on chaotic maps and affine transformation

There are three problems in the existing reversible data hiding (RDH) algorithms in encrypted images: one is that some algorithms are not processed in the encrypted domain; another is that the reversibility which implies exact data extraction and perfect image recovery cannot be ensured in some cases; the last is that data expansion occurs when probabilistic public-key cryptosystem is used for image encryption in some homomorphic schemes. In this paper, a complete RDH in encrypted images is proposed. By using the idea of cross division and additive homomorphism, we solve all of the problems. Experimental results verify the superiority of the proposed method, which will have a good potential for practical applications of multimedia privacy protection. HighlightsHost image is divided into crosses.Homomorphic encryption is with no data expansion.Data hiding is directly processed in encrypted domain.Real reversibility is realized.Difference histogram is shifted several rounds.

Reversible data hiding in encrypted images using cross division and additive homomorphism

We propose a tilt sensing scheme using a physical model with three sensitive axes of microelectromechanical systems (MEMS) accelerometers. Based on the physical model in which the gravitational acceleration is resolved into three components, we propose three numerical models to sense the tilt angle. First, two exact numerical models are presented to measure the gravitational acceleration and its one component along the tilt direction, respectively. The parameters of these two models are specific angles of the physical model, which can be used to assess the configuration of the physical model. Next, the measurement bias model is introduced to reduce the error resulting from the nonlinear relation between the gravitational acceleration and the tilt angle. Third, all the three numerical models are unified into a linear model whose parameters can be efficiently estimated using the least squares method. In the experiments, we evaluate the performance of the proposed scheme by examining the mean, the standard deviation, and the maximum of the errors. The experiment results show that our scheme is able to perform accurate tilt sensing with the average error below 0.1° in the measurement range (0°, 120°).

Accurate Tilt Sensing With Linear Model

Recently, an image scrambling scheme based on chaos theory and Vigenere cipher was proposed. The scrambling process is firstly to shift each pixel by sorting a chaotic sequence as Vigenere cipher, and then the pixel positions are shuffled by sorting another chaotic sequence. In this study, we analyze the security weakness of this scheme. By applying the combination of chosen-plaintext attack and differential attack, we propose two efficient cryptanalysis methods. Results show that all the keystream can be revealed. The original image scrambling scheme can be remedied by leveraging the MD5 hash value of the plain image as the initial condition of the chaotic system.

Cryptanalysis of image scrambling based on chaotic sequences and Vigenère cipher

A modified version of Zhang's reversible data hiding method in encrypted images is proposed in this letter. To make full use of spatial correlation in natural images, the former idea of block division is thoroughly abandoned, whereas the random diffusion strategy is used. Additionally, the fluctuation measurement of pixels containing embedded data is improved by accurate prediction. The experiment results reveal that our proposed method is superior to both Zhang's method and the later improved version proposed by Hong and others.

https://onlinelibrary.wiley.com/doi/pdf/10.4218/etrij.14.0213.0449

Hai Nan

Papers

Reversible data hiding in encrypted images using cross division and additive homomorphism

Accurate Tilt Sensing With Linear Model

Cryptanalysis of image scrambling based on chaotic sequences and Vigenère cipher

A Modified Reversible Data Hiding in Encrypted Images Using Random Diffusion and Accurate Prediction

GLS coding based security solution to JPEG with the structure of aggregated compression and encryption