Discrete Cosine Transform Coefficients Research Articles

Joint image splicing detection in DCT and Contourlet transform domain

Splicing is a fundamental and popular image forgery method and image splicing detection is urgently called for digital image forensics recently. In this paper, a Markov based approach is proposed to detect image splicing. The paper applies the Markov model in the block discrete cosine transform (DCT) domain and the Contourlet transform domain. First, the original Markov features of the inter-block between block DCT coefficients are improved by considering the different frequency ranges of each block DCT coefficients. Then, additional features are extracted in Contourlet transform domain to characterize the dependency of positions among Contourlet subband coefficients. And these features are extracted from single color channel for gray image while extracted from three color channels for color image. Finally, Support Vector Machines (SVMs) are exploited to classify the authentic and spliced images for the gray image dataset while ensemble classifier to the color image dataset. The experiment results demonstrate that the proposed detection scheme outperforms some state-of-the-art methods when applied to Columbia Image Splicing Detection Evaluation Dataset (DVMM), and ranks fourth in phase 1 on the Live Ranking of the first Image Forensics Challenge.

DWT-SVD and DCT based robust and blind watermarking scheme for copyright protection

In this article, a new DWT-SVD and DCT with Arnold Cat Map encryption based robust and blind watermarking scheme is proposed for copyright protection. The proposed scheme solves the most frequently occurring watermarking security problems in Singular Value Decomposition (SVD) based schemes which are unauthorized reading and false-positive detection. This scheme also optimizes fidelity and robustness characteristics. The grey image watermark splits into two parts using four bits MSBs and four bits LSBs of each pixel. Discrete Cosine Transform (DCT) coefficients of these MSBs and LSBs values are embedded into the middle singular value of each block having size 4 × 4 of the host image's one level Discrete Wavelet Transform (DWT) sub-bands. The reason for incorporating Arnold Cat Map in the proposed scheme is to encode the watermark image before embedding it in the host image. The proposed scheme is a blind scheme and does not require the choice of scaling factor. Thus, the proposed scheme is secure as well as free from the false positive detection problem. The proposed watermarking scheme is tested for various malicious and non-malicious attacks. The experimental results demonstrate that the scheme is robust, imperceptible and secure to several attacks and common signal processing operations.

Portable real-time DCT-based steganography using OpenCL

In this paper, a steganographic method for real-time data hiding is proposed. The main goal of the research is to develop steganographic method with increased robustness to unintentional image processing attacks. In addition, we prove the validity of the method in real-time applications. The method is based on a discrete cosine transform (DCT) where the values of a DCT coefficients are modified in order to hide data. This modification is invisible to a human observer. We further the investigation by implementing the proposed method using different target architectures and analyze their performance. Results show that the proposed method is very robust to image compression, scaling and blurring. In addition, modification of the image is imperceptible even though the number of embedded bits is high. The steganalysis of the method shows that the detection of the modification of the image is unreliable for a lower relative payload size embedded. Analysis of OpenCL implementation of the proposed method on four different target architectures shows considerable speedups.

Multiple Description Coding Based on Optimized Redundancy Removal for 3D Depth Map

Multiple description (MD) coding is a promising alternative for the robust transmission of information over error-prone channels. In 3D image technology, the depth map represents the distance between the camera and objects in the scene. Using the depth map combined with the existing multiview image, it can be efficient to synthesize images of any virtual viewpoint position, which can display more realistic 3D scenes. Differently from the conventional 2D texture image, the depth map contains a lot of spatial redundancy information, which is not necessary for view synthesis, but may result in the waste of compressed bits, especially when using MD coding for robust transmission. In this paper, we focus on the redundancy removal of MD coding based on the DCT (discrete cosine transform) domain. In view of the characteristics of DCT coefficients, at the encoder, a Lagrange optimization approach is designed to determine the amounts of high frequency coefficients in the DCT domain to be removed. It is noted considering the low computing complexity that the entropy is adopted to estimate the bit rate in the optimization. Furthermore, at the decoder, adaptive zero-padding is applied to reconstruct the depth map when some information is lost. The experimental results have shown that compared to the corresponding scheme, the proposed method demonstrates better rate central and side distortion performance.

Reorganized DCT-based image representation for reduced reference stereoscopic image quality assessment

In this paper, a novel reduced reference (RR) stereoscopic image quality assessment (SIQA) is proposed by characterizing the statistical properties of the stereoscopic image in the reorganized discrete cosine transform (RDCT) domain. Firstly, the difference image between the left and right view images is computed. Afterwards, the left and right view images, as well as the difference image, are decomposed by block-based discrete cosine transform (DCT). The DCT coefficients are further reorganized into a three-level coefficient tree, resulting in ten RDCT subbands. For each RDCT subband, the statistical property of the coefficient distribution is modeled by the generalized Gaussian density (GGD) function. And the mutual information (MI) and energy distribution ratio (EDR) are employed to depict the statistical properties across different RDCT subbands. Moreover, EDR can further model the mutual masking property of the human visual system (HVS). By considering the GGD modeling behavior within each RDCT subband and MI together EDR characterizing behavior across RDCT subbands, the statistical properties of the stereoscopic image are fully exploited, including the left view, right view, and the difference image. Experimental results demonstrated that the statistical properties of the difference image can well represent the perceptual quality of the stereoscopic image, which outperforms the representative RR quality metrics for stereoscopic image and even some full reference (FR) quality metrics. By considering the left view, right view, and difference image together, the performances of the proposed RR SIQA can be further improved, which presenting a more closely relationship between the quality metric output and human visual perception.

Information Theoretical Limit of Media Forensics: The Forensicability

While more and more forensic techniques have been proposed to detect the processing history of multimedia content, one starts to wonder if there exists a fundamental limit on the capability of forensics. In other words, besides keeping on searching what investigators can do, it is also important to find out the limit of their capability and what they cannot do. In this paper, we explore the fundamental limit of operation forensics by proposing an information theoretical framework. In particular, we consider a general forensic system of estimating operations’ hypotheses based on extracted features from the multimedia content. In this system, forensicability is defined as the maximum forensic information that features contain about operations. Then, due to its conceptual similarity with mutual information in an information theory, forensicability is measured as the mutual information between features and operations’ hypotheses. Such a measurement gives the error probability lower bound of all practical estimators, which use these features to detect the operations’ hypotheses. Furthermore, it can determine the maximum number of hypotheses that we can theoretically detect. To demonstrate the effectiveness of our proposed information theoretical framework, we apply this framework on a forensic example of detecting the number of JPEG compressions based on normalized discrete cosine transform (DCT) coefficient histograms. We conclude that, when subband (2, 3) is used in detection and the size of the testing database is <20000, the maximum number of JPEG compressions that we can expectedly perfectly detect using normalized DCT coefficient histogram features is four. Furthermore, we obtain the optimal strategies for investigators and forgers based on the fundamental measurement of forensicability.

Lagrangian twin support vector regression and genetic algorithm based robust grayscale image watermarking

A novel imperceptible, secure and robust grayscale image watermarking scheme using Lagrangian twin support vector regression (LTSVR) and genetic algorithm (GA) in discrete Cosine transform (DCT) domain is presented in this manuscript. Fuzzy entropy is used to select the relevant blocks for embedding the watermark. Selected number of blocks based on fuzzy entropy not only reduces the dimensionality of the watermarking problem but also discards redundant and irrelevant blocks. Significant DCT coefficients having high energy compaction property of each selected block are used to form the image dataset to train LTSVR to find the non-linear regression function between the input and target vector. The adaptive watermark strength, different for each selected block, is decided by the GA process based on well defined fitness function. Due to good learning capability of image characteristics and high generalization property of LTSVR, watermark is successfully extracted from the watermarked images against a series of image processing operations. From the experimental and comparison results performed on standard and real world images, it is inferred that the proposed method is suitable for copyright protection applications where high degree of robustness is desirable.

FITD: Fast Intra Transcoding from H.264/AVC to high efficiency video coding based on DCT coefficients and prediction modes

In the literatures, the designs of H.264 to High Efficiency Video Coding (HEVC) transcoders mostly focus on inter transcoding. In this paper, a fast intra transcoding system from H.264 to HEVC based on discrete cosine transform (DCT) coefficients and intra prediction modes, called FITD, is proposed by using the intra information retrieved from an H.264 decoder for transcoding. To design effective transcoding strategies, FITD not only refers block size of intra prediction and intra prediction modes, but also effectively uses the DCT coefficients to help a transcoder to predict the complexity of the blocks. We successfully use DCT coefficients as well as intra prediction information embedded in H.264 bitstreams to predict the coding depth map for depth limitation and early termination to simplify HEVC re-encoding process. After a HEVC encoder gets the prediction of a certain CU size from depth map, if it reaches the predicted depth, the HEVC encoder will stop the next CU branch. As a result, the numbers of CU branches and predictions in HEVC re-encoder will be substantially reduced to achieve fast and precise intra transcoding. The experimental results show that the FITD is 1.7–2.5 times faster than the original HEVC in encoding intra frames, while the bitrate is only increased to 3% or less and the PSNR degradation is also controlled within 0.1dB. Compared to the previous H.264 to HEVC transcoding approaches, FITD clearly maintains the better trade-off between re-encoding speed and video quality.

JPEG compression with recursive group coding

A task of alternative (faster and more efficient in compression ratio sense) coding of discrete cosine transform (DCT) coefficients within JPEG based image compression approach is considered. In the data processing chain, it is proposed to apply a recursive group coding (RGC) as an alternative to arithmetic or Huffmann coding. In contrast to the aforementioned data coding techniques, the RGC method is able to efficiently code symbols of very large alphabets (each block of 8x8 pixels of quantized DCT coefficients can be represented as such 64-byte or 128-byte symbol). Comparative analysis of efficiency for the standard JPEG and its proposed modification (for three images of three different digital cameras) is carried out using six different quantization tables. It is shown that RCG possesses low computational complexity and a high speed of compression simultaneously with higher compression ratio (CR) compared to the standard JPEG. The benefit in CR appears to be larger for smaller quantization steps (QSs) that mainly correspond to SHQ (super high quality) mode. This benefit can reach up to 10%. It is also demonstrated that the benefits exists for uniform quantization tables. The proposed coding method can be used for an additional compression of JPEG-images in coding traffic of communication lines. For this purpose, data of JPEG-images have to be partly decoded (till the level of the quantized DCT coefficients) and then recoded by RGC.

Identifying underground voids using a GPR circular-end bow-tie antenna system based on a support vector machine

ABSTRACTThis article presents a pattern recognition approach based on discrete cosine transform (DCT) and polarization attributes to identify voids underground in ground penetrating radar (GPR) images. Features vectors composed of DCT coefficients supplying to the support vector machine classifier are studied, together with their linearity. To evaluate the proposed method, we hold an experimental study of GPR data obtained by numerical modelling of a GPR circular-end bow-tie antenna system using the finite-integration technique based simulator. In order to form the database of features, different acquisition scenarios of GPR models are achieved by varying buried object’s material type, position, shape, size parameters, and electric characteristics of subsurface medium. The robustness of the proposed system towards degradation of the generated GPR data is tested by introducing Gaussian and speckle noise. The experimental results show the proposed approach exhibits encouraging performance in terms of void identification with errors of object apex position in GPR images.

Convolution Comparison Pattern: An Efficient Local Image Descriptor for Fingerprint Liveness Detection.

We present a new type of local image descriptor which yields binary patterns from small image patches. For the application to fingerprint liveness detection, we achieve rotation invariant image patches by taking the fingerprint segmentation and orientation field into account. We compute the discrete cosine transform (DCT) for these rotation invariant patches and attain binary patterns by comparing pairs of two DCT coefficients. These patterns are summarized into one or more histograms per image. Each histogram comprises the relative frequencies of pattern occurrences. Multiple histograms are concatenated and the resulting feature vector is used for image classification. We name this novel type of descriptor convolution comparison pattern (CCP). Experimental results show the usefulness of the proposed CCP descriptor for fingerprint liveness detection. CCP outperforms other local image descriptors such as LBP, LPQ and WLD on the LivDet 2013 benchmark. The CCP descriptor is a general type of local image descriptor which we expect to prove useful in areas beyond fingerprint liveness detection such as biological and medical image processing, texture recognition, face recognition and iris recognition, liveness detection for face and iris images, and machine vision for surface inspection and material classification.

Steganalysis Over Large-Scale Social Networks With High-Order Joint Features and Clustering Ensembles

This paper tackles a recent challenge in identifying culprit actors, who try to hide confidential payload with steganography, among many innocent actors in social media networks. The problem is called steganographer detection problem and is significantly different from the traditional stego detection problem that classifies an individual object as a cover or a stego. To solve the steganographer detection problem over large-scale social media networks, this paper proposes a method that uses high-order joint features and clustering ensembles. It employs 250-D features calculated from the high-order joint matrices of Discrete Cosine Transform (DCT) coefficients of JPEG images, which indicate the dependencies of image content. Furthermore, a number of hierarchical sub-clusterings trained by the features are integrated as a clustering ensemble based on the majority voting strategy, which is used to make optimal decisions on suspicious steganographers. Experimental results show that the proposed scheme is effective and efficient in identifying potential steganographers in large-scale social media networks, and has better performance when tested against the state-of-the-art steganographic methods.

Zero coefficient‐aware fast butterfly‐based inverse discrete cosine transform algorithm

The latest video coding standards, including Moving Picture Experts Group-4 (MPEG-4) advanced video coding (AVC)/H.264 and high-efficiency video coding (HEVC), use a discrete cosine transform (DCT) process as the core for compression efficiency, sacrificing the computational complexity at decoder. There have been a number of attempts to reduce the complexity of inverse DCT (IDCT). Butterfly-based factorisation remains the most commonly used method for such a reduction. In this study, the authors propose a zero (Z) coefficient-aware fast butterfly-based IDCT algorithm for video decoding. They focus on a reduction in the computational complexity of the butterfly-based 8 × 8 IDCT by removing the unnecessary computations of one-dimensional (1D) IDCT kernels, and adaptively applying IDCT kernels based on the number of non-Z DCT coefficients to speed-up 1D data. Their experimental results show that the average operation numbers using the proposed IDCT is approximately half that for the 8 × 8 IDCT implemented in the MPEG-4 AVC/H.264 and HEVC reference software. The improved computational complexity of the proposed method is demonstrated by measuring the running time, which requires only one-half of the IDCT time using the reference software.

Markov process-based retrieval for encrypted JPEG images

This paper develops a retrieval scheme for encrypted JPEG images based on a Markov process. In our scheme, the stream cipher and permutation encryption are combined to encrypt discrete cosine transform (DCT) coefficients for protecting JPEG image content's confidentiality. And thus, it is easy for the content owner to achieve the encrypted JPEG images uploaded to a database server. In the image retrieval stage, although the server does not know the plaintext content of a given encrypted query image, he can still extract image feature calculated from the transition probability matrices related to DCT coefficients, which indicate the intra-block, inter-block, and inter-component dependencies among DCT coefficients. And these three types of dependencies are modeled by the Markov process. After that, with the multi-class support vector machine (SVM), the feature of the encrypted query image can be converted into a vector with low dimensionality determined by the number of image categories. The encrypted database images are conducted similarly. After low-dimensional vector representation, the similarity between the encrypted query image and database image may be evaluated by calculating the distance of their corresponding feature vectors. At the client side, the returned encrypted images similar to the query image can be decrypted to the plaintext images with the help of the encryption key.

Rank-Based Image Watermarking Method With High Embedding Capacity and Robustness

This paper presents a novel rank-based method for image watermarking. In the watermark embedding process, the host image is divided into blocks, followed by the 2-D discrete cosine transform (DCT). For each image block, a secret key is employed to randomly select a set of DCT coefficients suitable for watermark embedding. Watermark bits are inserted into an image block by modifying the set of DCT coefficients using a rank-based embedding rule. In the watermark detection process, the corresponding detection matrices are formed from the received image using the secret key. Afterward, the watermark bits are extracted by checking the ranks of the detection matrices. Since the proposed watermarking method only uses two DCT coefficients to hide one watermark bit, it can achieve very high embedding capacity. Moreover, our method is free of host signal interference. This desired feature and the usage of an error buffer in watermark embedding result in high robustness against attacks. Theoretical analysis and experimental results demonstrate the effectiveness of the proposed method.

An Iterative Semi Blind Transform Domain Watermarking

Digital Watermarking is an efficient way of securing various multimedia contents. An Iterative semi-blind watermarking technique is proposed in this research for gray scale images, based on Transform Domain and Independent Component Analysis (ICA) Algorithms. Transform Domain includes Discrete Cosine Transform (DCT) and Singular Value Decomposition (SVD). In embedding process, the watermark is embedded into singular values of the host image's DCT coefficients. The host image and the secret image are considered as two separate signals and combined using the Fast-ICA Algorithm, a variant of Independent Component Analysis (ICA) mixing model, to generate the watermarked image and is also used to extract the watermark efficiently. The statistical property of the Fast-ICA Algorithm extracts the watermark differently at different time and hence the watermark is extracted repeatedly until the Normalized Cross Correlation (NCC) is above a certain threshold value to extract a clear watermark. The metrics Peak Signal to Noise Ratio (PSNR) and NCC are computed to measure the quality of watermarked image and extracted watermark respectively. This method increases the capacity, robustness and security than the Discrete Wavelet Transform (DWT) combined with SVD and Fast-ICA where the watermark is embedded and extracted from the low frequency band.

Scale invariant and fixed-length feature extraction by integrating discrete cosine transform and autoregressive signal modeling for palmprint identification

Recently, the need for automatic identification has caused researchers to focus on biometric identification methods. Palmprint-based biometric identification has several advantages such as user friendliness, low-cost capturing devices, and robustness. In this paper, a method that integrates the discrete cosine transform (DCT) and an autoregressive (AR) signal modeling is proposed for biometric identification. The method provides scale invariance and produces a fixed-length feature vector. In particular, the Burg algorithm is used for the determination of the AR parameters used as a feature vector. Experimental results demonstrate that a small number of the AR parameters that are modeling the DCT coefficients of a palmprint are sufficient to constitute a practically applicable identification system achieving a correct recognition rate of 99.79%. The accuracy of the proposed approach is not overly dependent on the number of training samples, another advantage of the method.

Đề xuất một số lược đồ nhúng tin và thủy vân dễ vỡ khóa công khai trên ảnh JPEG

This paper proposes some new schemes for data hiding and watermarking in JPEG images by using quantized discrete Cosine transform (DCT) blocks. The DCT coefficients are segmented into groups of continuous elements. For each group, r bits can be embedded by changing at most one element of the group. In comparison to some widely used data hiding schemes for JPEG images, our data hiding schemes achieve higher capability and better stego-image quality. The proposed public key watermarking method based on an embedded algorithm is capable to authenticate digital images.

English

Wavelet-based image compression algorithms have good performance and produce a rate scalable bitstream that can be decoded efficiently at several bit rates. Unfortunately, the discrete wavelet transform (DWT) has relatively high computational complexity. On the other hand, the discrete cosine transform (DCT) has low complexity and excellent compaction properties. Unfortunately, it is non-local, which necessitates implementing it as a block-based transform leading to the well-known blocking artifacts at the edges of the DCT blocks. This paper proposes a very fast and rate scalable algorithm that exploits the low complexity of DCT and the low complexity of the set partitioning technique used by the wavelet-based algorithms. Like JPEG, the proposed algorithm first transforms the image using block-based DCT. Then, it rearranges the DCT coefficients into a wavelet-like structure. Finally, the rearranged image is coded using a modified version of the SPECK algorithm, which is one of the best well-known wavelet-based algorithms. The modified SPECK consumes slightly less computer memory, has slightly lower complexity and slightly better performance than the original SPECK. The experimental results demonstrated that the proposed algorithm has competitive performance and high processing speed. Consequently, it has the best performance to complexity ratio among all the current rate scalable algorithms.

An integrated index for identification of fatty liver disease using radon transform and discrete cosine transform features in ultrasound images

Alcoholic and non-alcoholic fatty liver disease is one of the leading causes of chronic liver diseases and mortality in Western countries and Asia. Ultrasound image assessment is most commonly and widely used to identify the Non-Alcoholic Fatty Liver Disease (NAFLD). It is one of the faster and safer non-invasive methods of NAFLD diagnosis available in imaging modalities. The diagnosis of NAFLD using biopsies is expensive, invasive, and causes anxiety to the patients. The advent of advanced image processing and data mining techniques have helped to develop faster, efficient, objective, and accurate decision support system for fatty liver disease using ultrasound images. This paper proposes a novel feature extraction models based on Radon Transform (RT) and Discrete Cosine Transform (DCT). First, Radon Transform (RT) is performed on the ultrasound images for every 1 degree to capture the low frequency details. Then 2D-DCT is applied on the Radon transformed image to obtain the frequency features (DCT coefficients). Further the 2D-DCT frequency coefficients (features) obtained are converted to 1D coefficients vector in zigzag fashion. This 1D array of DCT coefficients are subjected to Locality Sensitive Discriminant Analysis (LSDA) to reduce the number of features. Then these features are ranked using minimum Redundancy and Maximum Relevance (mRMR) ranking method. Finally, highly ranked minimum numbers of features are fused using Decision Tree (DT), k-Nearest Neighbour (k-NN), Probabilistic Neural Network (PNN), Support Vector Machine (SVM), Fuzzy Sugeno (FS) and AdaBoost classifiers to get the highest classification performance. In this work, we have obtained an average accuracy, sensitivity and specificity of 100% in the detection of NAFLD using FS classifier. Also, we have devised an integrated index named as Fatty Liver Disease Index (FLDI) by fusing two significant LSDA components to distinguish normal and FLD class with single number.