Crypto-space reversible data hiding (RDH) has emerged as an effective technique for transmitting secret information over the Internet. However, most existing schemes are designed for uncompressed images, while almost all images are processed and transmitted in compressed formats. There is an urgent need to develop methods for compressed images, such as joint photographic experts group (JPEG). In this article, we propose an RDH in encrypted JPEG images, where the bitstreams of alternating current (AC) coefficients and the secret data are mapped to numbers over Galois field. The obtained numbers are then utilized to conduct a polynomial for secret sharing. By reproduction into secret shares, the AC coefficients and the secret data are secured. In addition, a block sorting strategy is used to reduce image distortion under low data payload. Experimental results demonstrate that the proposed scheme outperforms state-of-the-art methods in embedding capacity while preserving the file size and conforming to the JPEG format.

Secret image sharing (SIS) is a technique used to distribute confidential data by dividing it into multiple image shadows. Most of the existing approaches or algorithms protect confidential data by encryption with secret keys. This paper proposes a novel SIS scheme without using any secret key. The secret images are first quantized and encrypted by self-encryption into noisy ones. Then, the encrypted images are mixed into secret shares by cross-encryption. The image shadows are generated by replacing the lower bit-planes of the cover images with the secret shares. In the extraction phase, the receiver can restore the quantized secret images by combinatorial operations of the extracted secret shares. Experimental results show that our method is able to deliver a large amount of data payload with a satisfactory cover image quality. Besides, the computational load is very low since the whole scheme is mostly based on cycling-XOR operations.

Reversible data hiding in encrypted 3D models (RDHEM) is an emerging steganography technique, capable of both encrypting the cover model to ensure confidentiality and embedding additional messages for covert communication. However, the embedding capacity provided by recent RDHEM methods is still at a low level. In this paper, an adaptive vertex grouping strategy is proposed, which can divide the vertices in the cover 3D model into groups. Then, the multi-MSB prediction and Huffman coding are exploited to compress the data volume of vertices. Through proper vertex grouping and efficient data compression of the model vertices, the embedding capacity of the RDHEM can be effectively improved. Additionally, two schemes for 3D model encryption are provided. One is based on a secret sharing method over the Galois field and the other leverages the stream cipher technique. Experimental results show that the embedding capacity of the two proposed schemes significantly outperforms state-of-the-art schemes.

Image covert communication camouflages or conceals sensitive information in regular digital images during transmission to avoid detection or interception. It is widely used in military, intelligence, and law enforcement agencies. Data hiding is a feasible solution for covert communication, which embeds confidential information in cover media while preserving its visual appearance. In this letter, we propose an image reversible data hiding (RDH) scheme based on block regulation, which embeds secret data using pixel permutation. Traditional permutation-based data embedding methods usually suffer from error restoration of the cover image. To solve this problem, we propose a regulation operation to pre-process the image blocks, and thus most of the image blocks are converted into regular blocks. The regulated image blocks can be exploited to embed secret data without restoration error. In addition, we propose an adaptive strategy to generate permutation table dynamically, which further enhances the security level. Experimental results indicate that the proposed scheme provides a good embedding capacity and preserves a high level of image visual quality.

Reversible data hiding in encrypted images (RDHEI) can serve as a technical solution to secure data in applications that rely on cloud storage. The key features of an RDHEI scheme are reversibility, security, and data embedding rate. To enlarge the embedding rate, this paper proposes a novel RDHEI scheme based on the median edge detector (MED) and a new proposed hierarchical block variable length coding (HBVLC) technique. In our scheme, the image owner first predicts the pixel values of the carrier image with MED. Then, the prediction error array is sliced into bit-planes and encoded plane by plane. By leveraging the inherent features of the prediction error bit-planes, the image owner adaptively decomposes a bit-plane into blocks of different hierarchical levels based on its local smoothness and encodes the blocks with a variable length coding method. As a result, the carrier image is efficiently compressed to provide spare room for data embedding. The encoded carrier image is then processed with the conventional steps of an RDHEI technique. Experimental results show that the proposed scheme not only can restore the secret data and the carrier image without loss but also outperforms state-of-the-art methods in the embedding rate for images with various features.

Vector quantization (VQ) is a popular digital image compression technique. Its resulting index table can be further compressed using the side match vector quantization (SMVQ). In this research, we propose a reversible data hiding scheme based on the de-clustering rules to embed secret data during SMVQ compression. Referring to differently assigned codebooks, the de-clustering rules are equally applicable to both compressible and uncompressible VQ indices. The proposed scheme can produce a camouflaged VQ index table with a high payload. Besides, our scheme is free from the indicator bit, which is required in the conventional SMVQ. Experimental results are compared with state-of-the-art methods.

Nowadays, we greatly depend on Internet mass transmission, of all kinds of data, including critical information among them. Therefore, secure communication is an important topic. Data hiding can embed critical information into carriers, such as images, videos, and so on. Efficiently embedding information into images is the goal of this research. Image steganography techniques utilise a cover image to hide secret data and produce a stego-image by modifying pixel values. After modification, the stego-image is distorted with respect to the cover image. Vector quantisation (VQ) is a lossy image compression technique. The image recovered from the VQ compressed code has distortion compared to the original image. Based on the VQ image, we can hide secret data by modifying the pixel value in a way that the distortion is compensated. The embedding rate of the proposed scheme is adjustable. Experimental results show that our scheme can achieve a high embedding rate in comparison with related works. For low-quality VQ images, embedding can improve the visual quality of the stego-image at the same time.

As a commonly used image format, JPEG image is popularly used as the carrier for data hiding. After analyzing the virtue of the exploiting-modification-direction (EMD) scheme, which is designed for the spatial domain, this paper reforms its extraction function and applies it to the non-zero alternating current (AC) coefficients of JPEG images. Besides, a block and coefficient selection strategy is utilised to further improve the image quality and suppress the increase of file size. More specifically, the smooth discrete cosine transform (DCT) blocks with fewer zero AC coefficients are preferentially selected to embed data. The experiments indicate that our scheme achieves a high embedding capacity, meanwhile, the stego-image quality keeps good and the file size preserves well, moreover, our scheme is superior to the state-of-the-art scheme.

The covariance matrix of a pattern is composed by its second order central moments. For a rotationally symmetric shape, its covariance matrix is a scalar identity matrix. In this work, we apply this property to restore the skewed shape of rotational symmetry. The relations between the skew transformation matrix and the covariance matrices of original and skewed shapes are derived. By computing the covariance matrix of the skewed shape and letting the covariance matrix of the original shape be a scalar identity matrix, the skew transformation matrix can be solved. Then, the rotationally symmetric shape can be recovered by multiplying the inverse transformation matrix with the skewed shape. The method does not rely on continuous contours and is robust to noise, because only the second-order moments of the input shape are required. Experimental results are also presented.

We propose an algorithm to solve the path planning problem in an automated vehicle guidance system. Many methods have been proposed to solve such a problem, but, none of them takes as many practical factors into consideration. Although the literature dealt with one or more of the above factors separately, none had included them in a single research. We have included the following factors into our model: size and orientation, intended directions, driving costs, and obstacles. A powerful but simple algorithm based on the constrained distance transformation is shown to: (1) determine the feasibility given the start and finish positions; (2) find the optimal collision-free driving path which minimizes the total driving cost subject to the constraints; (3) provide a smooth path.

The covariance matrix of a rotationally symmetric shape is a scalar identity matrix. We apply this property of covariance matrix to deskew the skewed shape of rotational symmetry. The parameters of the deskew transformation matrix are solved by letting the covariance matrix of the transformed shape be equal to a scalar identity matrix. Then, the rotationally symmetric shape can be recovered by the deskew transformation matrix. The method does not rely on continuous contours, since only second-order moments of the input shape are required to be computed. Experimental results are also presented.

The Hough transform technique has been applied to detect rotational symmetry by Yip et al. (1994). He proposed an O(N/sup 3/) algorithm, where N is the number of edge points in the input image, to extract global or local features of rotational symmetry in the presence of noise and occlusion. In this paper, we follow the Yip et al. concept and design a simple and efficient algorithm, which can detect the rotational symmetry in O(N/sup 2/) time. Some simulation results of the new proposed algorithm are also given.

We propose a method to detect circular arcs, including the determinations of their centers, radii, lengths, and midpoints. The center, radius of a circular arc can easily be determined using circular Hough transform. But, the determinations of the arc midpoint and length are more complicated. Theoretical analysis of the centroid of the near-peak Hough transform data is presented. Then, the arc midpoint and length can be extracted from it.>