Blind JPEG steganalysis based on DCT coefficients differences

디지털 워터마킹은 저작권 보호와 멀티미디어 데이터 인증의 수단으로 사용되어 왔다. 기존의 공간영역에서의 CRT 기반 워터마킹 방법은 JPEG 압축에 취약하여, JPEG 압축에서 잘 동작하는 DCT 영역에서의 CRT 기반 워터마킹이 제안되었다. 본 논문에서는 DCT 영역에서의 향상된 CRT 기반 워터마킹 방법을 제안한다. 제안한 방법은 DCT 변환시 발생되는 rounding 오류 또는 여러 가지 공격에도 나머지들의 차에 대한 절대값의 변화를 감소시켜 견고성을 향상시킨다. 실험을 통하여 제안한 방법이 기존의 DCT 영역에서의 CRT 기반 워터마킹 방법보다 여러 가지 공격에 대하여 견고성이 향상되는 것을 확인하였다. Digital watermarking techniques have been used as one of the means for copyright protection and authentication of multimedia data. Conventional Chinese Remainder Theorem(CRT)-based spatial domain watermarking techniques do not perform well under JPEG compression. However, it is seen that the CRT-based watermarking technique in Discrete Cosine Transform(DCT) domain performs well for JPEG compression. In this paper, an improved CRT-based image watermarking method in the DCT domain is proposed. The proposed method provides better robustness which decreases changes of absolute difference of residues against rounding errors due to DCT conversion and various attacks. Experimental results show that the proposed method has a good robustness against various attacks compared with the conventional CRT-based watermarking in DCT domain.

Many effective watermarking algorithms have been proposed and implemented for digital images and digital video. However, a few algorithms have been proposed for audio watermarking. This is due to the fact that, human auditory system (HAS) is far more complex and sensitive than human visual system (HVS). In this research work, a new method of embedding image data into the audio signal and additive audio watermarking algorithm based on Discrete Cosine Transformation (DCT) domain is proposed. First, the original audio is transformed into DCT domain. The DCT coefficients are divided into a fixed number of subsections and the energy of each subsection is calculated. Next, watermark is generated from image by image processing algorithm. Watermarks are then embedded into selected peaks of highest energy subsection. Experimental results demonstrate that the watermark is inaudible and this algorithm is robust to common operations of digital audio signal processing, such as noise addition, re-sampling, requantization and so on. To evaluate the performance of the proposed audio watermarking method, subjective and objective quality tests including Bit Error Rate (BER) and Signal to Noise ratio (SNR) are conducted.

We routinely encounter digital color images that were previously compressed using the Joint Photographic Experts Group (JPEG) standard. En route to the image's current representation, the previous JPEG compression's various settings-termed its JPEG compression history (CH)-are often discarded after the JPEG decompression step. Given a JPEG-decompressed color image, this paper aims to estimate its lost JPEG CH. We observe that the previous JPEG compression's quantization step introduces a lattice structure in the discrete cosine transform (DCT) domain. This paper proposes two approaches that exploit this structure to solve the JPEG Compression History Estimation (CHEst) problem. First, we design a statistical dictionary-based CHEst algorithm that tests the various CHs in a dictionary and selects the maximum a posteriori estimate. Second, for cases where the DCT coefficients closely conform to a 3-D parallelepiped lattice, we design a blind lattice-based CHEst algorithm. The blind algorithm exploits the fact that the JPEG CH is encoded in the nearly orthogonal bases for the 3-D lattice and employs novel lattice algorithms and recent results on nearly orthogonal lattice bases to estimate the CH. Both algorithms provide robust JPEG CHEst performance in practice. Simulations demonstrate that JPEG CHEst can be useful in JPEG recompression; the estimated CH allows us to recompress a JPEG-decompressed image with minimal distortion (large signal-to-noise-ratio) and simultaneously achieve a small file-size.

Feature Selection is a preprocessing technique with great significance in data mining applications that aims at reducing computational complexity and increase predictive capability of a learning system. This paper presents a new hybrid feature selection algorithm based on Discrete Firefly optimization technique with dynamic alpha and gamma parameters and t-test filter technique to improve detectability of hidden message for Blind Image Steganalysis. The experiments are conducted on important dataset of feature vectors extracted from frequency domain, Discrete Cosine Transformation and Discrete Wavelet Transformation domain of cover and stego images. The results from popular JPEG steganography algorithms nsF5, Outguess, PQ and JP Hide and Seek show that proposed method is able to identify sensitive features and reduce the feature set by 67% in DCT domain and 37% in DWT domain. The experiment analysis shows that these algorithms are most sensitive to Markov features from DCT domain and variance statistical moment from DWT domain.

Support vector machine (SVM) regression is considered for a statistical method of single frame superresolution in both the spatial and Discrete Cosine Transform (DCT) domains. As opposed to current classification techniques, regression allows considerably more freedom in the determination of missing high-resolution information. In addition, since SVM regression approaches the superresolution problem as an estimation problem with a criterion of image correctness rather than visual acceptableness, its optimization results have better mean-squared error. With the addition of structure in the DCT coefficients, DCT domain image superresolution is further improved.

Nowadays, the majority of images are in JPEG and MPEG compressed formats, and JPEG2000 is considered to be the next generation of compression standard due to the high- performance of discrete wavelet transform (DWT). It is time- consuming and occupies too much memory in conventional image retrieval ways. In order to solve these problems, we use grading retrieval techniques to implement image retrieval based on discrete cosine transform (DCT) compressed domain and DWT compressed domain. For image retrieval based on DCT domain, we use color features: color moment and color histogram, to describe content of images and propose a new dynamic color space quantization based on color distribution; For image retrieval based on DWT domain, we use texture features as two level feature vectors. The mean and standard deviation of low frequency sub-band coefficients are used as the first level retrieval. The means and standard deviations of selected high frequency sub-band coefficients are used as the second level retrieval. Furthermore, the third level retrieval is achieved by the fast wavelet histogram. Our experiment results clearly show that the two grading image retrieval algorithms work better than other algorithms: store memory is reduced and retrieval accuracy is improved. Index Terms—Content based image retrieval (CBIR), compressed domain, discrete cosine transform (DCT), discrete wavelet transform (DWT), color features, texture features

The discrete cosine transform (DCT) offers superior energy compaction properties for a large class of functions and has been employed as a standard tool in many signal and image processing applications. However, it suffers from spurious behavior in the vicinity of edge discontinuities in piecewise smooth signals. To leverage the sparse representation provided by the DCT, in this article, we derive a framework for the inverse polynomial reconstruction in the DCT expansion. It yields the expansion of a piecewise smooth signal in terms of polynomial coefficients, obtained from the DCT representation of the same signal. Taking advantage of this framework, we show that it is feasible to recover piecewise smooth signals from a relatively small number of DCT coefficients with high accuracy. Furthermore, automatic methods based on minimum description length principle and cross-validation are devised to select the polynomial orders, as a requirement of the inverse polynomial reconstruction method in practical applications. The developed framework can considerably enhance the performance of the DCT in sparse representation of piecewise smooth signals. Numerical results show that denoising and image approximation algorithms based on the proposed framework indicate significant improvements over wavelet counterparts for this class of signals.

Just noticeable difference (JND), which reveals the visibility of our human visual system (HVS), is useful for image/video coding. Due to the content complexity, it is hard to accurately estimate the JND thresholds for different image blocks (e.g., edge and texture). Research on cognitive science indicates that the HVS is adaptive to extract the visual regularities for scene perception and understanding. Inspired by this, we analyze the effect of content complexity on visual masking, and calculate the content complexity with the distribution of orientation. Then, by considering the effect from content complexity, contrast sensitivity function, and luminance adaption, a novel JND model in Discrete Cosine Transform (DCT) domain is proposed. Experimental results demonstrate the effectiveness of the proposed model for JND estimation.

To meet with the different client end devices and network bandwidth transmission requirements, it is necessary to convert the high definition resolution images and videos to standard definition resolution format. A novel approach to convert image resolution with arbitrary ratios in the DCT (Discrete Cosine Transform) domain is proposed, which exploits the relationship of a block and its subblocks with differing size. It can realize arbitrary unequal ratios in the horizontal and vertical direction, respectively. Unlike the present methods, it does not need upsampling- downsampling process. It can perform downsizing directly to the original data and confirms to the standard decoder. The proposed approach is computationally fast and memory efficient and produces visually better images with higher PSNR compared to the spatial methods.

The performance and effectiveness of CBIR scheme are directly associated with construction of small dimensional as well as salient image features respectively. So, in this paper, we have carried out the image retrieval process with small dimensional salient Image components or features as compare to the original image size and the imaze retrieval accuracy has been improved due to the consideration of local information rather than global information of Image data. Initially, the Image data is exploited in block level by discrete cosine transformation (DCT) and subsequently, some significant DCT coefficients are selected from each block as salient image components. However, all the DCT coefficients are not equally important in terms of visual perception and their dimension is also not negligible even in Image retrieval process. Later, selected AC coefficients are divided into four different groups. Later, some statistical parameters are computed from each group. Each statistical value is placed at a particular matrix. So, from each group, the number of matrices constructed is equal to the number of statistical parameters evaluated and one more matrix for DC coefficients is considered. Further, for construction of small dimensional feature vectors, gray level co-occurrence matrix (GLCM) is employed on all constructed matrices to derive the feature vector for a color component. The same procedure is employed on all three components and all feature vectors are combined toaether to form the final feature vector. The proposed CBIR structure is tested on three standard Image database i.e. Corel-lK, GHIM-10K and Olivia database and the experimental results demonstrate satisfactorily image retrieval and performance outperform other state-of-art schemes in many instances with respect to their precision values.

Huge number of images is available on the internet. Efficient and effective retrieval system is needed to retrieve these images by the contents or features of the images like color, texture and shape. This system is called content based image retrieval (CBIR). Conventionally features are extracted from images in pixel domain. But at present almost all images are represented in compressed form using DCT (Discrete Cosine Transformation) blocks transformation. Some critical information is removed in compression and only perceptual information is left which has significant attraction for information retrieval in compressed domain. In this paper we study the problem that how to retrieve perceptual information in compressed domain JPEG such that to improve image retrieval. Our approach is based on quantized histogram statistical texture features in DCT blocks. We show that to get best image retrieval performance by extracting the statistical texture features of quantized histogram in DCT blocks using JPEG compressed format images. Experiments on the Corel animal database using the proposed approach, give results which show that the statistical texture features of histogram are robust in retrieval of images. This shows that texture features in local compression is a significant step for effective image retrieval.

The aim of image fusion is to combine similar information from multiple images into a single image. The methods which are based on discrete cosine transform (DCT) of image fusion are more competent and time-saving in real-time systems using DCT based standards of still Image. The existing DCT based methods are suffering from some side effects like blurring which can reduce the quality of the output image. To address this issue, the paper proposing new method for image fusion using Gaussian pyramid in DCT domain. The pyramid fusion provides better fusion quality. The execution time is extremely reduced, compare with existing methods. This method can be used for multi model image fusion as well as fusion of complementary images. The algorithm given in proposed system is simple and easy to implement. Also, it could be used for real time applications. The performance of our method is analyzed and compared with other image fusion methods. Experimental results show that there is no difference between the result of our method and water-based image fusion result. But our algorithm is carried out in DCT domain; it is efficient in processing time and simple.

The paper proposes an efficient transient audio coding algorithm based on Discrete Cosine Transform (DCT) and sine extraction with Matching Pursuit (MP), which can be used conveniently in the sinusoidal parametric audio coder. The overlapped input frame is firstly classified into transient and non-transient signal. The transient detection is designed based on the energy ratio of the overlapped subframe to the whole frame. The transient signal can be represented with sinusoidal model in the DCT domain. After DCT processing, the sinusoids are extracted based on MP algorithm. The transient coding parameters contain a series of amplitudes, frequencies and phases in the DCT domain. Finally, a 15.5kbps wide-band transient audio coder is designed and the synthesized transient signal performs a little better than the one coded by MPEG-4 sinusoidal parametric coder.

Presented is an efficient watermarking scheme using Particle Swarm Optimization (PSO) to watermark host fingerprint images with their corresponding facial images in the Discrete Cosine Transform (DCT) domain. PSO is used to find the best DCT coefficients’ locations in the host image where the facial image data can be embedded, so that the distortion produced in the host image is minimum. The objective function for PSO is formulated in terms of the Structural Similarity Index (SSIM) and the Orientation Certainty Level Index (OCL) so as to base it on the simple visual effect of the human visual perception capability and correct minutia prediction ability. The results exhibit better watermarked image quality while retaining the feature set of the original fingerprint. Moreover, the proposed technique is robust so that the extraction of watermark is possible even after the watermarked image is exposed to attacks. As a result, at the receiver’s end, the watermarked fingerprint image and the extracted facial image can be verified for a secure and accurate biometric based personal authentication.

A classification algorithm in the discrete cosine transform (DCT) domain for the classified vector quantization (CVQ) technique is proposed. The classifier employs four DCT coefficients of 4*4 subblock as edge-oriented features. The classifier is designed using a cluster-seeking algorithm to ensure that the centroid of a set of vectors in a class always belong to that class. Since the classification is performed in the DCT domain, this approach can be easily extended to the DCT transform coding technique. Simulation results show that a good visual quality of the coded image at fixed rates in the 0.625-0.813 b/pixel (bpp) range is obtained with comparable complexity. The weighted MSE (WMSE) analysis in conjunction with the proposed classifier is discussed.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>