Distribution Of DCT Coefficients Research Articles

A novel selective encryption scheme for H.264/AVC video with improved visual security

As cloud storage becomes more popular, concerns about data leakage have been increasing. Encryption techniques can be used to protect privacy of videos stored in the cloud. However, the recently proposed sketch attack for encrypted H.264/AVC video, which is based on the macroblock bitstream size (MBS), can generate the outline images of both intra-frames and inter-frames from a video encrypted by most existing encryption schemes; thus, the protection of the original video may be considered a failure. In this paper, a novel selective encryption scheme for H.264/AVC video with improved visual security is presented. Two different scrambling strategies that do not destroy the format compatibility are proposed to change the relative positions between macroblocks in intra-frames and inter-frames respectively, which in turn substantially distort the sketched outline images so that they do not disclose meaningful information. Moreover, the sign bits of non-zero DCT coefficients are encrypted to contribute to the visual security of our scheme, and an adaptive encryption key related to the intra prediction mode and the DCT coefficient distribution of each frame is employed to provide further security. The experimental results show that our encryption scheme can achieve a better visual scrambling effect with a small adverse impact on the video file size. Furthermore, the security analysis demonstrates that our scheme can successfully resist the MBS sketch attack compared with other related schemes. The proposed method is also proven secure against some other known attacks.

Improved block ordering and frequency selection for reversible data hiding in JPEG images

JPEG images are widely used in daily life. Reversible data hiding (RDH) in JPEG images is useful for many applications. How to avoid image distortion and preserve file size is very important for RDH in JPEG images. In this paper, a novel RDH scheme is proposed for JPEG images based on improved block ordering and frequency selection. The distortion that may be caused by the data embedding in each block is estimated more accurately based on the DCT coefficient distribution, and the blocks that result in less distortion will be firstly used for data embedding. Moreover, the non-zero AC coefficients with the frequencies having higher embedding efficiency will be used preferentially to further reduce image distortion. Consequently, less side information on frequency selection is needed for data extraction and image recovery with our scheme. Extensive experiments are conducted on several typical images and 500 images randomly selected from BOSSbase image database, and the numerical results demonstrate that better performance in terms of visual quality and file size preservation can be achieved with the proposed scheme compared to the state-of-the-art RDH schemes for JPEG images.

JPEG image tampering localization based on normalized gray level co-occurrence matrix

To locate the tampered region of double compressed JPEG images, one of the most effective methods is based on the statistical characteristic of the images. After tampering operation, the tampered region and the original region will have different statistical distributions. And according to this cue, the histogram of DCT coefficients can be moded as the mixture of the distributions of DCT coefficients in tampered and untampered regions. By estimating each distribution of the mixture model, the probability of being tampered of each DCT block can be calculated and the final localization result can be obtained. Thus the mixture model will significant impact the result. In this paper, a novel mixture model based on normalized gray level co-occurrence matrix (NGLCM) is proposed for tampering localization in JPEG images. Firstly, NGLCM is used to measure the conditional probabilities of being tampered regions and being untampered regions for each 8 × 8 DCT coefficient block, which can take advantage of both the statistical characteristic of double quantization effect and the relationship among neighboring blocks. Then the Bayesian posterior probability map is generated by the conditional probabilities, which indicates the tampering probability of each block. Finally, the map is refined based on a inter-block connectivity and Gaussian weighted filter strategy to determine the final tampered region location. Experimental results demonstrate that the proposed approach can localize the tampered region of JPEG images with a satisfactory performance and outperforms the state-of-the-art methods.

Statistical Detection of JPEG Traces in Digital Images in Uncompressed Formats

Intrinsic statistical properties of natural uncompressed images are used in image forensics for detecting the traces of previous processing operations. In this paper, we propose novel forensic detectors of JPEG compression traces in images stored in uncompressed formats, based on a theoretical analysis of Benford-Fourier coefficients computed on the $8\times 8$ block-Discrete Cosine Transform (DCT) domain. In fact, the distribution of such coefficients is derived theoretically both under the hypotheses of no compression and previous compression with a certain quality factor, allowing for the computation of the respective likelihood functions. Then, two classification tests based on different statistics are proposed, both relying on a discriminative threshold that can be determined without the need of any training phase. The statistical analysis is based on the only assumptions of generalized Gaussian distribution of DCT coefficients and independence among DCT frequencies, thus resulting in robust detectors applying to any uncompressed image. In fact, experiments on different datasets show that the proposed models are suitable for the images of different sizes and source cameras, thus overcoming dataset-dependence issues that typically affect the state-of-art techniques.

Global Contrast Enhancement Based Image Forensics Using Statistical Features

The evolution of modern cameras, mobile phones equipped with sophisticated image editing software has revolutionized digital imaging. In the process of image editing, contrast enhancement is a very common technique to hide visual traces of tampering. In our work, we have employed statistical distribution of block variance and AC DCT coefficients of an image to detect global contrast enhancement in an image. The variation in statistical parameters of block variance and AC DCT coefficients distribution for different degrees of contrast enhancement are used as features to detect contrast enhancement. An SVM classifier with 10-fold cross-validation is employed. An overall accuracy greater than 99% in detection with false rate less than 2% has been achieved. The proposed method is novel and it can be applied to uncompressed, previously JPEG compressed and post enhancement JPEG compressed images with high accuracy. The proposed method does not employ oft-repeated image histogram-based approach.

Image Reduction using Edge Based Region of Interest

Region of Interest in an image reduction is significant problem in DCT based image compression, which introduces the apparent distortion of visual quality. In this paper, an effective region of interest based reduction on an image is proposed. Compared with the existing method, first the edges are identified for compression. Secondly a range of ROI type is introduced according to DCT co-efficient distribution. An adaptive compound method is carried out to reduce the region on the image.

Malicious inter-frame video tampering detection in MPEG videos using time and spatial domain analysis of quantization effects

In this paper, a new algorithm is proposed for forgery detection in MPEG videos using spatial and time domain analysis of quantization effect on DCT coefficients of I and residual errors of P frames. The proposed algorithm consists of three modules, including double compression detection, malicious tampering detection and decision fusion. Double compression detection module employs spatial domain analysis using first significant digit distribution of DCT coefficients in I frames to detect single and double compressed videos using an SVM classifier. Double compression does not necessarily imply the existence of malignant tampering in the video. Therefore, malicious tampering detection module utilizes time domain analysis of quantization effect on residual errors of P frames to identify malicious inter-frame forgery comprising frame insertion or deletion. Finally, decision fusion module is used to classify input videos into three categories, including single compressed videos, double compressed videos without malicious tampering and double compressed videos with malicious tampering. The experimental results and the comparison of the results of the proposed method with those of other methods show the efficiency of the proposed algorithm.

An improved parameter estimation scheme for image modification detection based on DCT coefficient analysis

Most of the existing image modification detection methods which are based on DCT coefficient analysis model the distribution of DCT coefficients as a mixture of a modified and an unchanged component. To separate the two components, two parameters, which are the primary quantization step, Q1, and the portion of the modified region, α, have to be estimated, and more accurate estimations of α and Q1 lead to better detection and localization results. Existing methods estimate α and Q1 in a completely blind manner, without considering the characteristics of the mixture model and the constraints to which α should conform. In this paper, we propose a more effective scheme for estimating α and Q1, based on the observations that, the curves on the surface of the likelihood function corresponding to the mixture model is largely smooth, and α can take values only in a discrete set. We conduct extensive experiments to evaluate the proposed method, and the experimental results confirm the efficacy of our method.

Exploring DCT Coefficient Quantization Effects for Local Tampering Detection

In this paper, we focus on local image tampering detection. For a JPEG image, the probability distributions of its DCT coefficients will be disturbed by tampering operation. The tampered region and the unchanged region have different distributions, which is an important clue for locating tampering. Based on the assumption of Laplacian distribution of unquantized ac DCT coefficients, these two distributions as well as the size of tampered region can be estimated so that the probability of each DCT block being tampered is obtained. More accurate localization results could be got when we consider the prior knowledge of common tampered regions. We also design three kinds of features that can distinguish truly tampered regions from the false ones to reduce false alarm. For a tampered image which is saved in lossless compressed format, we also propose the specialized approach, which employs the quantization noise of high-frequency DCT coefficient, to improve the tampering localization performance. Extensive experiments on large scale databases prove the effectiveness of our proposed method and demonstrate that our method is suitable for locating tampered regions with different scales.

High capacity reversible data hiding scheme based upon discrete cosine transformation

In this paper, we propose a reversible data hiding scheme based on the varieties of coefficients of discrete cosine transformation of an image. Cover images are decomposed into several different frequencies, and the high-frequency parts are embedded with secret data. We use integer mapping to implement our 2-dimensional discrete cosine transformation. Thus, the image recovered from the modified coefficients can be transformed back to the correct data-hidden coefficients. Since the distribution of 2-dimensional DCT coefficients looks close to Gaussian distribution centralized at zero, it is a natural candidate for embedding secret data using the histogram shifting approach. Thus, our approach shifts the positive coefficients around zero to the right and the negative coefficients around zero to the left in order to leave a space to hide the secret data. The experimental comparisons show that, compared to Chang et al. and Lin et al.'s method, the embedding capacity and quality of the stego-image of the proposed method is a great improvement.

Secure steganography based on embedding capacity

Mostly the embedding capacity of steganography methods is assessed in non-zero DCT coefficients. Due to unequal distribution of non-zero DCT coefficients in images with different contents, images with the same number of non-zero DCT coefficients may have different actual embedding capacities. This paper introduces embedding capacity as a property of images in the presence of multiple steganalyzers, and discusses a method for computing embedding capacity of cover images. Using the capacity constraint, embedding can be done more secure than the state when the embedder does not know how much data can be hidden securely in an image. In our proposed approach, an ensemble system that uses different steganalyzer units determines the security limits for embedding in cover images. In this system, each steganalyzer unit is formed by a combination of multiple steganalyzers from the same type, which are sensitive to different payloads. The confidence of each steganalyzer on an image leads us to determine the upper bound of embedding rate for the image. Considering embedding capacity, the steganographer can minimize the risk of detection by selecting a proper cover image that is secure for a certain payload. Moreover, we analyzed the relation between complexity and embedding capacity of images. Experimental results showed the effectiveness of the proposed approach in enhancing the security of stego images.

Deinterlacing algorithm using edge direction from analysis of the DCT coefficient distribution

We propose a single-field deinterlacing algorithm with compressed domain edge direction information (CD-EDI). This algorithm was verified using the DCT coefficient distributions and pixel domain edge direction information (PD-EDI) in conjunction with an analysis of the neighboring pixels. The CD-EDI, which was calculated in the DCT domain, was first addressed, so that we could categorize the edge direction. We studied the distribution of DCT coefficients in a DCT-encoded block by taking into account the orientations of the four edge directions: 0 (horizontal), pi/4 (increase diagonal), iquest/2 (vertical), and 3pi/4 (decrease diagonal). In addition, we studied the PD-EDI, which we acquired by identifying the small pixel variations at the six edge directions. On the basis of an edge-based line average algorithm, a PDEDI was established within the operation window in order to lower the frequency of false judgments about the edge direction where a deinterlacing would be made. Finally, we propose an edge direction confidence (EDC) conception for each missing pixel, which is capable of revealing the accuracy of the edge detection. Since fuzzy sets can be represented by a membership function (MF), we used Gaussian MF to find EDC. The proposed algorithm has a simple EDC-evaluating structure of low complexity, which therefore makes it easy for implementation in hardware. In the extensive simulations conducted for different test sequences, the proposed algorithm outperformed all of the other state-of-the-art deinterlacing methods.

Operational distortion–quantization curve-based bit allocation for smooth video quality

Quality fluctuation has a major negative effect on subjective video quality. A desirable single-pass frame-level constant-distortion bit allocation scheme is proposed in the paper for smooth video quality throughout the video sequence. The average distortion of all previous coded frames is taken as the target distortion for the current frame. According to the linear rate control algorithm [Z. He, S.K. Mitra, A linear source model and a unified rate control algorithm for DCT video coding, IEEE Trans. Circuits Syst. Video Technol. 12 (2002) 970–982; Z. He, S.K. Mitra, Optimum bit allocation and accurate rate control for video coding via ρ domain source modeling, IEEE Trans. Circuits Syst. Video Technol. 12 (2002) 840–849], the distortion and rate are exponential and linear functions of the number of zero coefficients, respectively. Based on the distribution of the absolute DCT coefficients, an operational distortion–quantization stepsize ( D– Q) relationship curve can be established quickly. With the target distortion and operational D– Q curve, we then derive the close-form formulas for estimating the number of zero coefficients and the slope θ in the linear rate model, as well as the bit budget for the current frame. Experimental results show that the proposed constant-distortion (but variable rate) bit allocation scheme provides much smoother video quality on all test video sequences than the constant bit allocation scheme that has been used in many standard video coding reference software.

Distribution shape of two-dimensional DCT coefficients of natural images

The univariate distribution of DCT coefficients of natural images is investigated. The probability density function (PDF) of the coefficients is modelled with the generalised Gaussian function (GGF) which includes the Gaussian and the Laplacian PDF as special cases. The shape parameter of the GGF is estimated according to the maximum likelihood principle, χ2 tests of fit showed that GGFs model the distribution of DCT coefficients more accurately than Laplacian PDFs.

Statistical distributions of image DCT coefficients

Several assumptions regarding the distributions of the DCT coefficients of image data have been made in recent years. A recent paper by Reininger and Gibson shows on the basis of distribution tests that the non-DC coefficients are best approximated by a Laplacian distribution. In this paper a similar test is used to show that while the distributions of the non-DC coefficients depend on the type of image, for most images, if a single distribution is desired for all the non-DC coefficients, a Cauchy distribution provides a better fit.