Yun-Qing Shi

Pairwise Prediction-Error Expansion for Efficient Reversible Data Hiding

In prediction-error expansion (PEE) based reversible data hiding, better exploiting image redundancy usually leads to a superior performance. However, the correlations among prediction-errors are not considered and utilized in current PEE based methods. Specifically, in PEE, the prediction-errors are modified individually in data embedding. In this paper, to better exploit these correlations, instead of utilizing prediction-errors individually, we propose to consider every two adjacent prediction-errors jointly to generate a sequence consisting of prediction-error pairs. Then, based on the sequence and the resulting 2D prediction-error histogram, a more efficient embedding strategy, namely, pairwise PEE, can be designed to achieve an improved performance. The superiority of our method is verified through extensive experiments.

Forensics feature analysis in quaternion wavelet domain for distinguishing photographic images and computer graphics

In this paper, a novel set of features based on Quaternion Wavelet Transform (QWT) is proposed for digital image forensics. Compared with Discrete Wavelet Transform (DWT) and Contourlet Wavelet Transform (CWT), QWT produces the parameters, i.e., one magnitude and three angles, which provide more valuable information to distinguish photographic (PG) images and computer generated (CG) images. Some theoretical analysis are done and comparative experiments are made. The corresponding results show that the proposed scheme achieves 18 percents’ improvements on the detection accuracy than Farid’s scheme and 12 percents than Özparlak’s scheme. It may be the first time to introduce QWT to image forensics, but the improvements are encouraging.

Structural Design of Convolutional Neural Networks for Steganalysis

Recent studies have indicated that the architectures of convolutional neural networks (CNNs) tailored for computer vision may not be best suited to image steganalysis. In this letter, we report a CNN architecture that takes into account knowledge of steganalysis. In the detailed architecture, we take absolute values of elements in the feature maps generated from the first convolutional layer to facilitate and improve statistical modeling in the subsequent layers; to prevent overfitting, we constrain the range of data values with the saturation regions of hyperbolic tangent (TanH) at early stages of the networks and reduce the strength of modeling using 1×1 convolutions in deeper layers. Although it learns from only one type of noise residual, the proposed CNN is competitive in terms of detection performance compared with the SRM with ensemble classifiers on the BOSSbase for detecting S-UNIWARD and HILL. The results have implied that well-designed CNNs have the potential to provide a better detection performance in the future.

New Channel Selection Rule for JPEG Steganography

In this paper, we present a new channel selection rule for joint photographic experts group (JPEG) steganography, which can be utilized to find the discrete cosine transform (DCT) coefficients that may introduce minimal detectable distortion for data hiding. Three factors are considered in our proposed channel selection rule, i.e., the perturbation error (PE), the quantization step (QS), and the magnitude of quantized DCT coefficient to be modified (MQ). Experimental results demonstrate that higher security performance can be obtained in JPEG steganography via our new channel selection rule.

Reversible Data Hiding: Advances in the Past Two Decades

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 Image Data Hiding with Contrast Enhancement

In this letter, a novel reversible data hiding (RDH) algorithm is proposed for digital images. Instead of trying to keep the PSNR value high, the proposed algorithm enhances the contrast of a host image to improve its visual quality. The highest two bins in the histogram are selected for data embedding so that histogram equalization can be performed by repeating the process. The side information is embedded along with the message bits into the host image so that the original image is completely recoverable. The proposed algorithm was implemented on two sets of images to demonstrate its efficiency. To our best knowledge, it is the first algorithm that achieves image contrast enhancement by RDH. Furthermore, the evaluation results show that the visual quality can be preserved after a considerable amount of message bits have been embedded into the contrast-enhanced images, even better than three specific MATLAB functions used for image contrast enhancement.

A reversible data hiding method with contrast enhancement for medical images

Reversible data hiding with contrast enhancement is performed on medical images.Pre-processing is improved so that better visual quality can be obtained.Principal values in background are differentiated to improve image quality.Better contrast enhancement effects than the previous work are achieved. In this paper, a reversible data hiding method with contrast enhancement is presented for medical images. Firstly, image background segmentation is performed and the principal gray-scale values in the segmented background are identified. By excluding the corresponding histogram bins from being expanded for data hiding, the contrast of region of interest (ROI) in medical images can be selectively enhanced. Considering the characteristics of pixel distribution, we develop a new pre-processing strategy to reduce the visual distortions that may be caused. With the proposed method, an original image can be exactly recovered from the corresponding enhanced image by hiding the side information within it. The experimental results on a set of medical images show that the visibility of ROI can be improved. Compared with the previous method, the proposed method can achieve more contrast enhancement effects and better visual quality for medical images.

An integer wavelet transform based scheme for reversible data hiding in encrypted images

In this paper, a novel reversible data hiding (RDH) scheme for encrypted digital images using integer wavelet transform, histogram shifting and orthogonal decomposition is presented. This scheme takes advantage of the Laplacian-like distribution of integer wavelet high-frequency coefficients in high frequency sub-bands and the independence of orthogonal coefficients to facilitate data hiding operation in encrypted domain, and to keep the reversibility. Experimental results has demonstrated that this scheme outperforms all of other existing RDH schemes in encrypted domain in terms of higher PSNR at the same amount of payload. Compared with the state-of-the-arts, the proposed scheme can be applied to all natural images with higher embedding rate.

An Effective Method for Detecting Double JPEG Compression With the Same Quantization Matrix

Detection of double JPEG compression plays an important role in digital image forensics. Some successful approaches have been proposed to detect double JPEG compression when the primary and secondary compressions have different quantization matrices. However, detecting double JPEG compression with the same quantization matrix is still a challenging problem. In this paper, an effective error-based statistical feature extraction scheme is presented to solve this problem. First, a given JPEG file is decompressed to form a reconstructed image. An error image is obtained by computing the differences between the inverse discrete cosine transform coefficients and pixel values in the reconstructed image. Two classes of blocks in the error image, namely, rounding error block and truncation error block, are analyzed. Then, a set of features is proposed to characterize the statistical differences of the error blocks between single and double JPEG compressions. Finally, the support vector machine classifier is employed to identify whether a given JPEG image is doubly compressed or not. Experimental results on three image databases with various quality factors have demonstrated that the proposed method can significantly outperform the state-of-the-art method.

Detecting Covert Channels in Computer Networks Based on Chaos Theory

Covert channels via the widely used TCP/IP protocols have become a new challenging issue for network security. In this paper, we analyze the information hiding in TCP/IP protocols and propose a new effective method to detect the existence of hidden information in TCP initial sequence numbers (ISNs), which is known as one of the most difficult covert channels to be detected. Our method uses phase space reconstruction to create a processing space called reconstructed phase space, where a statistical model is proposed for detecting covert channels in TCP ISNs. Based on the model, a classification algorithm is developed to identify the existence of information hidden in ISNs. Simulation results have demonstrated that our proposed detection method outperforms the state-of-the-art technique in terms of high detection accuracy and greatly reduced computational complexity. Instead of offline processing as the state-of-the-art does, our new scheme can be used for online detection.