13948777
Steganalysis Based on Multiple Features Formed by Statistical Moments of Wavelet Characteristic Functions
In this paper, a steganalysis scheme based on multiple features formed by statistical moments of wavelet characteristic functions is proposed. Our theoretical analysis has pointed out that the defined n-th statistical moment of a wavelet characteristic function is related to the n-th derivative of the corresponding wavelet histogram, and hence is sensitive to data embedding. The selection of the first three moments of the characteristic functions of wavelet subbands of the three-level Haar wavelet decomposition as well as the test image has resulted in total 39 features for steganalysis. The effectiveness of the proposed system has been demonstrated by extensive experimental investigation. The detection rate for Cox et al.'s non-blind spread spectrum (SS) data hiding method, Piva et al.'s blind SS method, Huang and Shi's 8×8 block SS method, a generic LSB method (as embedding capacity being 0.3 bpp), and a generic QIM method (as embedding capacity being 0.1 bpp) are all above 90% over all of the 1096 images in the CorelDraw image database using the Bayes classifier. Furthermore, when these five typical data hiding methods are jointly considered for steganalysis, i.e., when the proposed steganalysis scheme is first trained sequentially for each of these five methods, and is then tested blindly for stego-images generated by all of these methods, the success classification rate is 86%, thus pointing out a new promising approach to general blind steganalysis. The detection results of steganalysis on Jsteg, Outguess and F5 have further demonstrated the effectiveness of the proposed steganalysis scheme.
11148752
Steganalysis Using High-Dimensional Features Derived from Co-occurrence Matrix and Class-Wise Non-Principal Components Analysis (CNPCA)
This paper presents a novel steganalysis scheme with high-dimensional feature vectors derived from co-occurrence matrix in either spatial domain or JPEG coefficient domain, which is sensitive to data embedding process. The class-wise non-principal components analysis (CNPCA) is proposed to solve the problem of the classification in the high-dimensional feature vector space. The experimental results have demonstrated that the proposed scheme outperforms the existing steganalysis techniques in attacking the commonly used steganographic schemes applied to spatial domain (Spread-Spectrum, LSB, QIM) or JPEG domain (OutGuess, F5, Model-Based).
1704202
Effective steganalysis based on statistical moments of wavelet characteristic function
In this paper, an effective steganalysis based on statistical moments of wavelet characteristic function is proposed. It decomposes the test image using two-level Haar wavelet transform into nine subbands (here the image itself is considered as the LL/sub 0/ subband). For each subband, the characteristic function is calculated. The first and second statistical moments of the characteristic functions from all the subbands are selected to form an 18-dimensional feature vector for steganalysis. The Bayes classifier is utilized in classification. All of the 1096 images from the CorelDraw image database are used in our extensive experimental work. With randomly selected 100 images for training and the remaining 996 images for testing, the proposed steganalysis system can steadily achieve a correct classification rate of 79% for the non-blind Spread Spectrum watermarking algorithm proposed by Cox et ai, 88% for the blind Spread Spectrum watermarking algorithm proposed by Piva et ai, and 91% for a generic LSB embedding method, thus indicating significant advancement in steganalysis.
4242083
Lossless Data Hiding Using Histogram Shifting Method Based on Integer Wavelets
This paper proposes a histogram shifting method for image lossless data hiding in integer wavelet transform domain. This algorithm hides data into wavelet coefficients of high frequency subbands. It shifts a part of the histogram of high frequency wavelet subbands and thus embeds data by using the created histogram zero-point. This shifting process may be sequentially carried out if necessary. Histogram modification technique is applied to prevent overflow and underflow. The performance of this proposed technique in terms of the data embedding payload versus the visual quality of marked images is compared with that of the existing lossless data hiding methods implemented in the spatial domain, integer cosine transform domain, and integer wavelet transform domain. The experimental results have demonstrated the superiority of the proposed method over the existing methods. That is, the proposed method has a larger embedding payload in the same visual quality (measured by PSNR (peak signal noise ratio)) or has a higher PSNR in the same payload.
17381912
Hydrogen-bonding interactions in the active site of a low molecular weight protein-tyrosine phosphatase
Molecular dynamics simulation of the Michaelis complex, phospho-enzyme intermediate, and the wild-type and C12S mutant have been carried out to examine hydrogen-bonding interactions in the active site of the bovine low molecular weight protein-tyrosine phosphatase (BPTP). It was found that the Sγ atom of the nucleophilic residue Cys-12 is ideally located at a position opposite from the phenylphosphate dianion for an inline nucleophilic substitution reaction. In addition, electrostatic and hydrogen-bonding interactions from the backbone amide groups of the phosphate-binding loop strongly stabilize the thiolate anion, making Cys-12 ionized in the active site. In the phospho-enzyme intermediate, three water molecules are found to form strong hydrogen bonds with the phosphate group. In addition, another water molecule can be identified to form bridging hydrogen bonds between the phosphate group and Asp-129, which may act as the nucleophile in the subsequent phosphate hydrolysis reaction, with Asp-129 serving as a general base. The structural difference at the active site between the wild-type and C12S mutant has been examined. It was found that the alkoxide anion is significantly shifted toward one side of the phosphate binding loop, away from the optimal position enjoyed by the thiolate anion of the wild-type enzyme in an SN2 process. This, coupled with the high pKa value of an alcoholic residue, makes the C12S mutant catalytically inactive. These molecular dynamics simulations provided details of hydrogen bonding interactions in the active site of BPTP, and a structural basis for further studies using combined quantum mechanical and molecular mechanical potential to model the entire dephosphorylation reaction by BPTP. c © 2000 John Wiley & Sons, Inc. J Comput Chem 21: 1192–1203, 2000
31972087
Sparse data aggregation in sensor networks
18339723
Automatic video object shape extraction and its classification with camera in motion
In this paper, we present an automatic moving object extraction and classification system. For automatic extraction of object taken by a moving camera, a novel technique is proposed, in which optical flow handles the background modeling and camera motion estimation, and frame difference information yields the exact object shape. We also use forward region boundary based change detection approach for frame difference. This approach assures change detection for uniform intensity regions. For classification, weighted likelihood discriminant based shape classifier is designed. Unlike maximum likelihood (ML) methods, our proposed method utilizes information from all classes to design the classifier. In the description phase of the classifier, curvature features are extracted from the shape and are utilized to build a hidden Markov model (HMM). The HMM provides a robust ML description of the shape. In the discrimination phase, a weighted likelihood discriminant function is introduced, which weights the likelihoods of curvature at individual points of the shape to minimize the classification error. The weights are estimated by generalized probabilistic descent (GPD) method. To demonstrate the performance of the proposed method, we present results achieved for car shapes extraction and classification.
40124869
Image Pattern Recognition Based on Examples - A Combined Statistical and Structural-Syntactic Approach
An application of combined statistical and structural-syntactic approach in Chinese character recognition is presented. The algorithm adopts a structural representation for Chinese characters, but in the classification and training process, the structural matching and parameter adjustment is conducted in a statistical way. Different from the conventional structural approaches, in this system, only a few predefined "knowledge" is required. In most cases, knowledge acquisition is simplified to "memorization" of examples, and the parameters for classification can be refined using statistical training. In this way it avoids the main difficulties inherent in the implementation of classification systems based on structural features. Compared with conventional statistical algorithms, the algorithm is based on a structural model of image patterns, so it has approximately all the advantages of structural pattern recognition algorithms. A prototype system has been realized based on this strategy, and the effectiveness of the method is verified by the experimental results.
9663679
The Use of SVM for Chinese New Word Identification
We present a study of new word identification (NWI) to improve the performance of a Chinese word segmenter. In this paper the distribution and types of new words are discussed empirically. In particular, we focus on the new words of two surface patterns, which account for more than 80% of new words in our data sets: NW11 (two-character new word) and NW21 (a bi-character word followed with a single character). NWI is defined as a problem of binary classification. A statistical learning approach based on a SVM classifier is used. Different features for NWI are explored, including in-word probability of a character (IWP), the analogy between new words and lexicon words, anti-word list, and frequency in documents. The experiments show that these features are useful for NWI. The F-scores of NWI we achieved are 64.4% and 54.7% for NW11 and NW21, respectively. The overall performance of the Chinese word segmenter could be improved by Roov 24.5% and F-score 6.5% in PK-close test of the 1st SIGHAN bakeoff. This achieves the performance of state-of-the-art word segmenters.