This chapter introduces the subject of statistical pattern recognition (SPR). It starts by considering how features are defined and emphasizes that the nearest neighbor algorithm achieves error rates comparable with those of an ideal Bayes’ classifier. The concepts of an optimal number of features, representativeness of the training data, and the need to avoid overfitting to the training data are stressed. The chapter shows that methods such as the support vector machine and artificial neural networks are subject to these same training limitations, although each has its advantages. For neural networks, the multilayer perceptron architecture and back-propagation algorithm are described. The chapter distinguishes between supervised and unsupervised learning, demonstrating the advantages of the latter and showing how methods such as clustering and principal components analysis fit into the SPR framework. The chapter also defines the receiver operating characteristic, which allows an optimum balance between false positives and false negatives to be achieved.

Statistical Pattern Recognition

Computational Analysis of Microarray Data

Hyperspectral sensors record the reflectance from the Earth's surface over the full range of solar wavelengths with high spectral resolution. The resulting high-dimensional data contain rich information for a wide range of applications. However, for a specific application, not all the measurements are important and useful. The original feature space may not be the most effective space for representing the data. Feature mining, which includes feature generation, feature selection (FS), and feature extraction (FE), is a critical task for hyperspectral data classification. Significant research effort has focused on this issue since hyperspectral data became available in the late 1980s. The feature mining techniques which have been developed include supervised and unsupervised, parametric and nonparametric, linear and nonlinear methods, which all seek to identify the informative subspace. This paper provides an overview of both conventional and advanced feature reduction methods, with details on a few techniques that are commonly used for analysis of hyperspectral data. A general form that represents several linear and nonlinear FE methods is also presented. Experiments using two widely available hyperspectral data sets are included to illustrate selected FS and FE methods.

Feature Mining for Hyperspectral Image Classification

This paper models the messages embedded by spatial least significant bit (LSB) matching as independent noises to the cover image, and reveals that the histogram of the differences between pixel gray values is smoothed by the stego bits despite a large distance between the pixels Using the characteristic function of difference histogram (DHCF), we prove that the center of mass of DHCF (DHCF COM) decreases after messages are embedded Accordingly, the DHCF COMs are calculated as distinguishing features from the pixel pairs with different distances The features are calibrated with an image generated by average operation, and then used to train a support vector machine (SVM) classifier The experimental results prove that the features extracted from the differences between nonadjacent pixels can help to tackle LSB matching as well

Steganalysis of LSB matching using differences between nonadjacent pixels

This paper presents a learning-based steganalysis/detection method to attack spatial domain least significant bit LSB matching steganography in grayscale images, which is the antetype of many sophisticated steganographic methods. We model the message embedded by LSB matching as the independent noise to the image, and theoretically prove that LSB matching smoothes the histogram of multi-order differences. Because of the dependency among neighboring pixels, histogram of low order differences can be approximated by Laplace distribution. The smoothness caused by LSB matching is especially apparent at the peak of the histogram. Consequently, the low order differences of image pixels are calculated. The co-occurrence matrix is utilized to model the differences with the small absolute value in order to extract features. Finally, support vector machine classifiers are trained with the features so as to identify a test image either an original or a stego image. The proposed method is evaluated by LSB matching and its improved version "Hugo". In addition, the proposed method is compared with state-of-the-art steganalytic methods. The experimental results demonstrate the reliability of the new detector. Copyright © 2013 John Wiley & Sons, Ltd.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/sec.864

Steganalysis of least significant bit matching using multi-order differences

https://www.shsu.edu/~qxl005/New/Publications/PR_lsbmatching.pdf

Feature mining and pattern classification for steganalysis of LSB matching steganography in grayscale images

Image complexity and feature mining for steganalysis of least significant bit matching steganography

In this paper, we present a scheme for steganalysis of LSB matching steganography based on feature extraction and pattern recognition techniques. Shape parameter of Generalized Gaussian Distribution (GGD) in the wavelet domain is introduced to measure image complexity. Several statistical pattern recognition algorithms are applied to train and classify the feature sets. Comparison of our method and others indicates our method is highly competitive. It is highly efficient for color image steganalysis. It is also efficient for grayscale steganalysis in the low image complexity domain.

Image Complexity and Feature Extraction for Steganalysis of LSB Matching Steganography

Despite significant efforts by merchants, card issuers and law enforcement to curb fraud, online fraud continues to plague electronic commerce web sites. More advanced solutions are desired to protect merchants from the constantly evolving problem caused by fraud. The supervised machine learning technique for the most well known fraud detection algorithms makes them inadequate for an online system, which usually contains a mammoth size of non-stationery data. This paper describes a method to dynamically create user profile for the purpose of fraud detection. We use a data mining algorithm to adaptively profile legitimate customer behaviour in a form of association rule set from a transaction database. Then the incoming transactions are compared against the user profile to indicate the anomalies. A novel pattern match approach is proposed to evaluate how unusual the new transactions are. An empirical evaluation shows that we can accurately differentiate the anomaly behaviour from profiled user behaviour.

Behaviour Mining for Fraud Detection.

The proliferation of malware (viruses, Trojans, and other malicious code) in recent years has presented a serious threat to individual users, enterprises, and organizations alike. Current static scanning techniques for malware detection have serious limitations; on the other hand, sandbox testing fails to provide a complete satisfactory solution either due to time constraints (e.g., time bombs cannot be detected before its preset time expires). What is making the situation worse is the ease of producing polymorphic (or variants of) computer viruses that are even more complex and difficult than their original versions to detect. In this paper, we propose a new approach for detecting polymorphic malware in the Windows platform. Our approach rests on an analysis based on the Windows API calling sequence that reflects the behaviour of a particular piece of code. The analysis is carried out directly on the PE (portable executable) code. It is achieved in two basic steps: construct the API calling sequences for both the known virus and the suspicious code, and then perform a similarity measurement between the two sequences after a sequence realignment operation is done. An alternative technique based on comparing the bags of API calls, and the technique’s performance, are also studied. Favourable (in terms of time and accuracy of detection) experimental results are obtained and presented.

Jianyun Xu

Papers

Feature mining and pattern classification for steganalysis of LSB matching steganography in grayscale images

Image complexity and feature mining for steganalysis of least significant bit matching steganography

Image Complexity and Feature Extraction for Steganalysis of LSB Matching Steganography

Behaviour Mining for Fraud Detection.

Obfuscated Malicious Executable Scanner