Embed Message Bits Research Articles

Image data hiding with multi-scale autoencoder network

Image steganography is the process of hiding information which can be text, image, or video inside a cover image. The advantage of steganography over cryptography is that the intended secret message does not attract attention and is thus more suitable for secret communication in a highly-surveillant environment such as civil disobedience movements. Internet memes in social media and messaging apps have become a popular culture worldwide, so this folk custom is a good application scenario for image steganography. We try to explore and adopt the steganography techniques on the Internet memes in this work. We implement and improve the HiDDeN model by changing the Conv-BN-ReLU blocks convolution layer with a multiscale autoencoder network so that the neural network learns to embed message bits in higher-level feature space. Compared to methods that convolve feature filters on the row-pixel domain, our proposed MS-Hidden network learns to hide secrets in both low-level and high-level image features. As a result, the proposed model significantly reduces the bit error rate to empirically 0% and the required network parameters are much less than the HiDDeN model.

Modified Multiway Pixel-Value Differencing Methods Based on General Quantization Ranges for Image Steganography

A modified multiway pixel-value differencing method for image steganography using general quantization ranges of pixel pairs&#x2019; difference values is proposed, where the widths of the quantization ranges are not limited to be powers of two. The method can be employed to embed a secret message into a cover image with a higher embedding rate. The cover image is partitioned into non-overlapping complete or incomplete blocks using <inline-formula> <tex-math notation="LaTeX">$1 \times 3$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$2 \times 2$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$2 \times 3$ </tex-math></inline-formula>, or <inline-formula> <tex-math notation="LaTeX">$3 \times 3$ </tex-math></inline-formula> block templates. The message bitstream, whose bit positions are randomized in advance by use of a random number generator, can be embedded in the resulting blocks. More specifically, one pixel in each block is set as a shared pixel, and the least-significant-bit substitution method with the optimal pixel adjustment process is employed to embed message bits into the shared pixel. Next, the shared pixel in the block is combined with each remaining pixel to form a pixel pair, which is then utilized to embed message bits by a new pixel-value differencing method. Also, the use of non-power-of-two range widths is accomplished by a multiple-based number conversion mechanism, and the shared pixel in each block, as well as each related pixel pair whose two grayscale values pass a falling-off-boundary check, are used to embed the digits of the resulting multiple-based number. The experimental results show that the embedding rates yielded by the proposed method are higher than those yielded by existing multiway pixel-value differencing methods. The stego-images resulting from embedding secret messages retain good image quality, and the RS steganalysis process cannot detect the presence of the embedded secret messages.

Quantum Representation of Indexed Images and its Applications

Indexed images have been widely used in classical digital image processing, however there have been no reports on the representation and applications of indexed images in quantum image processing so far. To solve the representation problem of indexed images on a quantum computer, a quantum indexed image representation (QIIR) method is proposed in the paper. A quantum indexed image consists of a quantum data matrix and a quantum palette matrix. Each data structure is based on the basic states of qubit sequence to represent information, including pixel positions and pixel values in the data matrix, and indexes and color values in the palette matrix. Several simple geometric and color transformations of quantum indexed images are presented later, including orthogonal rotation, cyclic shift, color inversion, color replacement and color look-up. Finally, a quantum indexed image steganography based on EzStego is proposed. In this scheme, the distance between two arbitrary color values in the quantum palette is first calculated, and then several effective color pairs are obtained. At last, according to embedded message bits, pixel values in the data matrix are updated in light of effective color pairs, and a new quantum data matrix with embedded message is obtained. The proposed scheme can be executed on a future quantum computer. The feasibility and validness of this scheme are verified on a classical computer from four aspects: visual quality, embedding capacity, robustness and security.

Improving the Robustness of Adaptive Steganographic Algorithms Based on Transport Channel Matching

Moving steganography and steganalysis from the laboratory into the real world, the robustness of steganography needs to be further considered. In this paper, we propose a robust steganographic algorithm to resist the JPEG compression of transport channel based on transport channel matching. Transport channel matching can adjust images to meet the requirements of transport channel so that the impact of JPEG compression from the channel can be reduced. To improve the robustness of steganography, the embedded message bits will be encoded by the error correction code. Then, the adaptive steganographic algorithms will be used to embed messages. To enhance the coding rate, the error correction capability ${t}$ of the error correction code is dynamically adjusted according to the images. Experimental results on the local simulation of JPEG compression and social network site demonstrate that the proposed steganographic algorithm has a good performance with respect to both robustness and security.

Vertex angle image watermarking with optimal detector

This paper presents a robust image watermarking method based on geometric modeling. Four samples of wavelet approximation transform on each image block along with the mean value of other coefficients on that block are modeled as three points in 2-D space. The length and width coordinate of the point associated with the mean value are equal. Two line segments with a shared endpoint can be drawn using these three points. The vertex angle formed between the line segments is utilized as a watermarking variable. In order to embed message bits, the vertex angle is altered by displacing of points. To preserve the imperceptibility of the watermark, geometrical tools are elegantly used to minimize the embedding distortion. Moreover, Maximum likelihood decoder is implemented at the receiver side. To this end, the probability density function of the noisy embedded angles is well approximated by the Beta distribution for wavelet approximation coefficients of images. Due to embedding in the vertex angle, the proposed scheme is not vulnerable to the gain attack. Using the low frequency components of the image blocks and the mean value of each block makes the proposed method robust to the noise and compression attacks. Experimental results confirm the validity of the theoretical analyses given in the paper and show the superiority of the method in contrast to similar techniques in this field.

Adaptive image steganography using contourlet transform

This work presents adaptive image steganography methods which locate suitable regions for embedding by contourlet transform, while embedded message bits are carried in discrete cosine transform coefficients. The first proposed method utilizes contourlet transform coefficients to select contour regions of the image. In the embedding procedure, some of the contourlet transform coefficients may change which may cause errors at the message extraction phase. We propose a novel iterative procedure to resolve such problems. In addition, we have proposed an improved version of the first method in which it uses an advanced embedding operation to boost its security. Experimental results show that the proposed base method is an imperceptible image steganography method with zero retrieval error rate. Comparisons with other steganography methods which utilize contourlet transform show that our proposed method is able to retrieve all messages perfectly, whereas the others fail. Moreover, the proposed method outperforms the ContSteg method in terms of PSNR and the higher-order statistics steganalysis method. Experimental evaluations of our methods with the well known DCT-based steganography algorithms have demonstrated that our improved method has superior performance in terms of PSNR and SSIM, and is more secure against the steganalysis attack.

Optimized LSB Matching Steganography Based on Fisher Information

This paper proposes an optimized LSB matching steganography based on Fisher Information. The embedding algorithm is designed to solve the optimization problem, in which Fisher information is the objective function and embedding transferring probabilities are variables to be optimized. Fisher information is the quadratic function of the embedding transferring probabilities, and the coefficients of quadratic term are determined by the joint probability distribution of cover elements. By modeling the groups of elements in a cover image as Gaussian mixture model, the joint probability distribution of cover elements for each cover image is obtained by estimating the parameters of Gaussian mixture distribution. For each sub-Gaussian distribution in Gaussian mixture distribution, the quadratic term coefficients of Fisher information are calculated, and the optimized embedding transferring probabilities are solved by quadratic programming. By maximum posteriori probability principle, cover pixels are classified as the categories corresponding to sub-Gaussian distributions. At last, in order to embed message bits, pixels chose to add or subtract one according to the optimized transferring probabilities of the category. The experiments show that the security performance of this new algorithm is better than the existing LSB matching.

Multiscale Fragile Watermarking Based on the Gaussian Mixture Model

In this paper, a new multiscale fragile watermarking scheme based on the Gaussian mixture model (GMM) is presented. First, a GMM is developed to describe the statistical characteristics of images in the wavelet domain and an expectation-maximization algorithm is employed to identify GMM model parameters. With wavelet multiscale subspaces being divided into watermarking blocks, the GMM model parameters of different watermarking blocks are adjusted to form certain relationships, which are employed for the presented new fragile watermarking scheme for authentication. An optimal watermark embedding method is developed to achieve minimum watermarking distortion. A secret embedding key is designed to securely embed the fragile watermarks so that the new method is robust to counterfeiting, even when the malicious attackers are fully aware of the watermark embedding algorithm. It is shown that the presented new method can securely embed a message bit stream, such as personal signatures or copyright logos, into a host image as fragile watermarks. Compared with conventional fragile watermark techniques, this new statistical model based method modifies only a small amount of image data such that the distortion on the host image is imperceptible. Meanwhile, with the embedded message bits spreading over the entire image area through the statistical model, the new method can detect and localize image tampering. Besides, the new multiscale implementation of fragile watermarks based on the presented method can help distinguish some normal image operations such as JPEG compression from malicious image attacks and, thus, can be used for semi-fragile watermarking.