Jiangqun Ni

Robust digital image stabilization using the Kalman filter

In this paper, we present a new digital image stabilization (DIS) algorithm based on feature point tracking. Feature points well tracked by the Kanade-Lucas-Tomasi (KLT) tracker are used to estimate the global motion between two consecutive image frames. The motion prediction with the Kalman filter (KF) is incorporated into the KLT tracker to further speed up the tracking process. Moreover, we develop an adaptive KF to better handle the intentional motion of the camera. In addition, a new scheme is also proposed to detect the scene change and automatically change the reference frame. Experimental simulation shows that the proposed DIS algorithm has the characteristics of high accuracy, good robustness to different irregular conditions.

Uniform Embedding for Efficient JPEG Steganography

Steganography is the science and art of covert communication, which aims to hide the secret messages into a cover medium while achieving the least possible statistical detectability. To this end, the framework of minimal distortion embedding is widely adopted in the development of the steganographic system, in which a well designed distortion function is of vital importance. In this paper, a class of new distortion functions known as uniform embedding distortion function (UED) is presented for both side-informed and non side-informed secure JPEG steganography. By incorporating the syndrome trellis coding, the best codeword with minimal distortion for a given message is determined with UED, which, instead of random modification, tries to spread the embedding modification uniformly to quantized discrete cosine transform (DCT) coefficients of all possible magnitudes. In this way, less statistical detectability is achieved, owing to the reduction of the average changes of the first- and second-order statistics for DCT coefficients as a whole. The effectiveness of the proposed scheme is verified with evidence obtained from exhaustive experiments using popular steganalyzers with various feature sets on the BOSSbase database. Compared with prior arts, the proposed scheme gains favorable performance in terms of secure embedding capacity against steganalysis.

An efficient JPEG steganographic scheme using uniform embedding

Steganography is the science and art of covert communication. Its objective is to hide the most secret messages into a cover object with the least possible statistical detectability. In practice, this is generally realized by a framework of minimal distortion embedding. This paper presents an efficient JPEG steganographic scheme based on syndrome trellis coding (STC) and a uniform embedding strategy, which, instead of random modification, tries to modify nonzero quantized DCT coefficients of different magnitude with equal probability, leading to possible minimal artifacts for statistics of DCT coefficients as a whole. The distortion metric corresponding to the uniform embedding is based on the magnitude of the DCT coefficients and both their intra- and inter-block neighborhood coefficients and known as uniform embedding distortion metric (UED). With the proposed scheme, the STC provides multiple codewords for a given message, while the UED determines the best one with minimal distortion. In this way, the average statistics change in each bin is significantly reduced, which corresponds to less detectability of steganalysis. Compared with prior arts, experimental results demonstrate the superior performance of the proposed scheme in terms of secure embedding capacity against steganalysis.

Region duplication detection based on Harris corner points and step sector statistics

Region duplication is a simple and effective operation for digital image forgeries. The detection of region duplication is very important in digital image forensics. Most existing detection methods for region duplication are based on exhaustive block-matching of image pixels or transform coefficients. They may not be effective when the duplicate regions have gone through some geometrical transformations. In this paper, a novel region duplication detection method that is robust to general geometrical transformations is proposed. Firstly, the Harris corner interest points in an image are detected. Then, an image region description method based on step sector statistics is developed to represent the small circle image region around each Harris point with a feature vector. Finally, the small circle image regions are matched using the best-bin-first algorithm to reveal duplicate regions. Experimental results show that the proposed method can work effectively on the forged images from two image databases, and it is also robust to several geometrical transformations and image degradations.

An efficient JPEG steganographic scheme based on the block entropy of DCT coefficients

Steganography is the art of covert communication. This paper presents an efficient JPEG steganography scheme based on the block entropy of DCT coefficients and syndrome trellis coding (STC). The proposed cost function explores both the block complexity and distortion effects due to flipping and rounding errors. The STC provides multiple solutions to embed messages to a block of coefficients. The proposed scheme determines the best one with minimal distortion effect. In this way, the total distortions are significantly reduced, which corresponds to less detectability of steganalysis. Compared with similar schemes, experiment results demonstrate the superior performance of the proposed scheme in terms of secure embedding capacity against steganalysis.

An Informed Watermarking Scheme Using Hidden Markov Model in the Wavelet Domain

Achieving robustness, imperceptibility and high capacity simultaneously is of great importance in digital watermarking. This paper presents a new informed image watermarking scheme with high robustness and simplified complexity at an information rate of 1/64 bit/pixel. Firstly, a Taylor series approximated locally optimum test (TLOT) detector based on the hidden Markov model (HMM) in the wavelet domain is developed to tackle the problem of unavailability of exact embedding strength in the receiver due to informed embedding. Then based on the TLOT detector and the concept of dirty-paper code design, new HMM-based spherical codes are constructed to provide an effective tradeoff between robustness and distortion. The process of informed embedding is formulated as an optimization problem under the robustness and distortion constraints and the genetic algorithm (GA) is then employed to solve this problem. Moreover, the perceptual distance in the wavelet domain is also developed and incorporated into the GA-based optimization. Simulation results demonstrate that the proposed informed watermarking algorithm has high robustness against common attacks in signal processing and shows a comparable performance to the state-of-the-art scheme with a greatly reduced arithmetic complexity.

Deep Learning Hierarchical Representations for Image Steganalysis

Nowadays, the prevailing detectors of steganographic communication in digital images mainly consist of three steps, i.e., residual computation, feature extraction, and binary classification. In this paper, we present an alternative approach to steganalysis of digital images based on convolutional neural network (CNN), which is shown to be able to well replicate and optimize these key steps in a unified framework and learn hierarchical representations directly from raw images. The proposed CNN has a quite different structure from the ones used in conventional computer vision tasks. Rather than a random strategy, the weights in the first layer of the proposed CNN are initialized with the basic high-pass filter set used in the calculation of residual maps in a spatial rich model (SRM), which acts as a regularizer to suppress the image content effectively. To better capture the structure of embedding signals, which usually have extremely low SNR (stego signal to image content), a new activation function called a truncated linear unit is adopted in our CNN model. Finally, we further boost the performance of the proposed CNN-based steganalyzer by incorporating the knowledge of selection channel. Three state-of-the-art steganographic algorithms in spatial domain, e.g., WOW, S-UNIWARD, and HILL, are used to evaluate the effectiveness of our model. Compared to SRM and its selection-channel-aware variant maxSRMd2, our model achieves superior performance across all tested algorithms for a wide variety of payloads.

Using Statistical Image Model for JPEG Steganography: Uniform Embedding Revisited

Uniform embedding was first introduced in 2012 for non-side-informed JPEG steganography, and then extended to the side-informed JPEG steganography in 2014. The idea behind uniform embedding is that, by uniformly spreading the embedding modifications to the quantized discrete cosine transform (DCT) coefficients of all possible magnitudes, the average changes of the first-order and the second-order statistics can be possibly minimized, which leads to less statistical detectability. The purpose of this paper is to refine the uniform embedding by considering the relative changes of statistical model for digital images, aiming to make the embedding modifications to be proportional to the coefficient of variation. Such a new strategy can be regarded as generalized uniform embedding in substantial sense. Compared with the original uniform embedding distortion (UED), the proposed method uses all the DCT coefficients (including the DC, zero, and non-zero AC coefficients) as the cover elements. We call the corresponding distortion function uniform embedding revisited distortion (UERD), which incorporates the complexities of both the DCT block and the DCT mode of each DCT coefficient (i.e., selection channel), and can be directly derived from the DCT domain. The effectiveness of the proposed scheme is verified with the evidence obtained from the exhaustive experiments using a popular steganalyzer with rich models on the BOSSbase database. The proposed UERD gains a significant performance improvement in terms of secure embedding capacity when compared with the original UED, and rivals the current state-of-the-art with much reduced computational complexity.

Rate and Distortion Optimization for Reversible Data Hiding Using Multiple Histogram Shifting

Histogram shifting (HS) embedding as a typical reversible data hiding scheme is widely investigated due to its high quality of stego-image. For HS-based embedding, the selected side information, i.e., peak and zero bins, usually greatly affects the rate and distortion performance of the stego-image. Due to the massive solution space and burden in distortion computation, conventional HS-based schemes utilize some empirical criterion to determine those side information, which generally could not lead to a globally optimal solution for reversible embedding. In this paper, based on the developed rate and distortion model, the problem of HS-based multiple embedding is formulated as the one of rate and distortion optimization. Two key propositions are then derived to facilitate the fast computation of distortion due to multiple shifting and narrow down the solution space, respectively. Finally, an evolutionary optimization algorithm, i.e., genetic algorithm is employed to search the nearly optimal zero and peak bins. For a given data payload, the proposed scheme could not only adaptively determine the proper number of peak and zero bin pairs but also their corresponding values for HS-based multiple reversible embedding. Compared with previous approaches, experimental results demonstrate the superiority of the proposed scheme in the terms of embedding capacity and stego-image quality.

An efficient reversible data hiding scheme using prediction and optimal side information selection

In this paper, we present an efficient histogram shifting (HS) based reversible data hiding scheme for copyright protection of multimedia. Firstly, an improved HS based multi-layer embedding process for rhombus prediction is employed by introducing a control parameter to explore the correlation of prediction errors. A rate-distortion model for HS embedding is then developed for optimal side information selection, which is especially suitable for low payload reversible data hiding when only a single layer embedding is required. Finally, a modified location map is constructed to facilitate the compression of location map and further increase the embedding capacity. Compared with similar schemes, experimental results demonstrate the superior performance of the proposed scheme in the terms of embedding capacity and stego-image quality.