We present a framework of the application of Principal Component Analysis (PCA) to automatically obtain meaningful
metrics from intrusion detection measurements. In particular, we report the progress made in applying PCA to analyze
the behavioral measurements of malware and provide some preliminary results in selecting dominant attributes from an
arbitrary number of malware attributes. The results will be useful in formulating an optimal detection threshold in the
principal component space, which can both validate and augment existing malware classifiers.
We present a statistical footprint-based method to characterize several symmetric cryptographic primitives as they are
used in lightweight digital image encryption. In particular, using spatial-domain histogram and frequency-domain image
analysis techniques, we identify a number of metrics from the encrypted images and use them to contrast the security
performance of different cryptographic primitives. For each of the metrics, the best performing cryptographic primitive
is identified. Complementary primitives are then combined to result in a product cipher with better cryptographic
performance.
We propose a phase-encoding-based digital watermarking
technique for fingerprint template protection and verification. We
extract a signature from the one-dimensional Fourier phase of the
original fingerprint image (template) and then embed it back into the
image using a variation of phase-shift keying modulation and the
spread-spectrum method. To minimize the degradation to important
minutiae features of fingerprint data, we segment the template into a
region of interest (ROI) and a region of background (ROB) and embed
the watermark adaptively in these two regions using an adaptive
phase quantization method. We give experimental results with
fingerprint biometric data that demonstrate the protection of fingerprint
templates as well as the verification of fingerprint
recognition.
We propose a composite signature-based digital watermarking technique for fingerprint verification and identity extraction applications. In a two-step process, our scheme attempts to integrate three modalities of authentication-"what you know," "what you have," and "what you are." Using a signature extracted from the Fourier phase of an original image, we hide an encoded signature back into the original image using a frequency domain bit-plane embedding technique. Additionally, other identity information is embedded in the spatial domain using a spread-spectrum method. The detection process computes the Fourier transform of the watermarked image, then extracts the embedded signature and correlates it with a calculated signature. The embedding of a composite signature ensures robustness against any chosen form of distortion. Simulation results for the signature and identity embedding and detection are provided for the Fingerprint Verification Competition (FVC) fingerprint image database.
The goal of this project is to use computer analysis to classify small lung nodules, identified on CT, into likely benign
and likely malignant categories. We compared discrete wavelet transforms (DWT) based features and a modification of
classical features used and reported by others. To determine the best combination of features for classification, several
intensities of white noise were added to the original images to determine the effect of such noise on classification
accuracy. Two different approaches were used to determine the effect of noise: in the first method the best features for
classification of nodules on the original image were retained as noise was added. In the second approach, we
recalculated the results to reselect the best classification features for each particular level of added noise. The CT images
are from the National Lung Screening Trial (NLST) of the National Cancer Institute (NCI). For this study, nodules were
extracted in window frames of three sizes. Malignant nodules were cytologically or histogically diagnosed, while benign
had two-year follow-up. A linear discriminant analysis with Fisher criterion (FLDA) approach was used for feature
selection and classification, and decision matrix for matched sample to compare the classification accuracy. The initial
features mode revealed sensitivity to both the amount of noise and the size of window frame. The recalculated feature
mode proved more robust to noise with no change in terms of classification accuracy. This indicates that the best
features for computer classification of lung nodules will differ with noise, and, therefore, with exposure.
Using CT images from the National Lung Screening Trial (NLST) of the National Cancer Institute (NCI), interpreted by radiologists at the Georgetown University, our goal was to investigate the feature extraction method using discrete wavelet transform (DWT) and to demonstrate their potential in distinguishing between benign and malignant nodule status. We analyzed multiple 2 mm thick slices of 40 subjects with benign nodules and 7 subjects with malignant
nodules for a total of 112 and 78 slices, respectively. Data was analyzed in the region-of-interest (ROI) that included nodule and surrounding areas in three different-sized windows. A linear discriminant analysis (LDA) of wavelets coefficients was used for data analysis. In particular we examined discriminative power of the wavelet based features using Fisher LDA, and evaluated the classification results using decision matrix (DM) for matched sample (MS). For visualization we used 3-D Heat Maps, originally developed in MATLAB(R) (MathWorks, Natick, MA) for gene expression array analysis, modified to display the magnitude of similarities between cases under analysis. The use of DWT in the image pre-processing modules resulted in a significant improvement in discrimination between benign and malignant nodules. The results show better classification accuracy with the DWT based features, as compared to
previously proposed classification features (p-values: 0.008, 0.022, and 0.039, depending on window size). The Heat Maps provide useful data visualization for further investigation as they have the ability to identify cases that should be further explored to understand why some of the benign nodules look similar to malignant in the wavelet domain.
In this work, we address phase-signature based digital image watermarking. The signature is extracted from the Fourier phase information of the digital media. It is then embedded in the Fourier magnitude spectrum. The detection and/or authentication is based on the well established area of phase-only filter based correlation techniques in the optics community. We propose to analyze the distortion coming out of the embedding process, model it, and eventually parameterize it so that optimal embedding can be done by trading off with other aspects of watermarking. It will be shown why permutation like functions facilitate the signature embedding process by minimizing the embedding degradation.
Steganographic and watermarking information inserted into a color image file, regardless of embedding algorithm, causes disturbances in the relationships between neighboring pixels. A method for steganalysis utilizing the local binary pattern (LBP) texture operator to examine the pixel texture patterns within neighborhoods across the color planes is presented. Providing the outputs of this simple algorithm to an artificial neural net capable of supervised learning results in the creation of a surprisingly reliable predictor of steganographic content, even with relatively small amounts of embedded data. Other tools for identifying images with steganographic content have been developed by forming a neural network input vector comprised of image statistics that respond to particular side effects of specific embedding algorithms. The neural net in our experiment is trained with general texture related statistics from clean images and images modified using only one embedding algorithm, and is able to correctly discriminate clean images from images altered by data embedded by one of various different watermarking and steganographic algorithms. Algorithms tested include various steganographic and watermarking programs and include spatial and transform domain image hiding techniques. The interesting result is that clean color images can be reliably distinguished from steganographically-altered images based on texture alone, regardless of the embedding algorithm.
We present the further development of a watermarking technique that embeds an authentication signal in an image. In this paper, we concentrate on the JPEG 2000 image format. The detection/extraction of this signal can then be used to decide whether the image has gone through any intentional malicious tampering. Therefore, the watermark needs to be fragile to such tampering attacks. On the other hand, we need to make sure that the authentication is robust to change resulting from the watermarking process itself, or from necessary changes such as image compression.
We address the robustness against watermarking process issue in two ways. First, we decompose the image into phase and magnitude values. A signature is then generated from the phase values. In particular, binary phase-only filters and their variants will are utilized for this. This signature is subsequently hidden into the magnitude part by a bit-plane embedding technique. The disjoint operations of signature generation and signature embedding minimize the embedding artifacts of the authentication signal. Secondly, we use wavelet decomposition, whereby, the signature can be generated from one subband, and then it can be embedded in other subband(s), or the same subband.
We propose a correlation-based digital watermarking technique for robust image pattern authentication. We hide a phase-based signature of the image back into its Fourier magnitude spectrum in the embedding stage. The detector computes the Fourier transform of the watermarked image and extracts the embedded signature. Authentication performance is measured by a correlation test of the extracted signature and the signature computed from the watermarked image. The quality of the watermarked image is obtained from the peak signal-to-noise ratio metric. We also furnish simulation results to show the robustness of our approach to typical image processing as found in JPEG compression
In this paper we address the reliable transmission of security-enabled multimedia data over the internet which is becoming increasingly vulnerable to a variety of cyber-attacks. Due to their real-timeliness aspect, multimedia data in Internet mostly uses User Datagram Protocol(UDP) as the transport media as opposed to the Transport Control Protocol (TCP). UDP is inherently an unreliable transport media that results in certain unacknowledged packet losses. Multimedia applications usually can tolerate some packet losses for its rendering at the receiver side. But, for the security-enhanced multimedia that we are talking about, reliability of reception of most of the packets within a certain tolerance time need to be guaranteed. This is where we come in with a new protocol that ensures packet-level reliability as well as stream-level authentication of multimedia.
Speckle noise and phase errors are two major a sources of quality degradation for synthetic aperture radar imageries. In this work, we address this problem with the proposal of a spatio-temporal metric to benchmark these degradations by analyzing an azimuthal image sequence. Preliminary results of the metrics with and without multiresolutional formulation are reported.
This paper addresses the problems associated with the dynamic change detection of an image sequence in the compressed domain. In particular, wavelet compression is considered here. With its multi-resolutional decomposition there are many different routes of image compression with wavelets. This paper will present some preliminary results of different compression schemes on spatio-temporal change detection metrics.
This paper proposes to use a multiresolutional spatio- temporal metric for the segmentation of an image sequence. In particular, scene-cut detection performance from an image sequence will be furnished. Wavelet decomposition is used for the multiresolutional analysis. The segmentation results obtained here can be used in the video browsing and indexing in multimedia applications.
This paper introduces a metric called Velocital Information Content (VIC) which is used to chart quality variations in digital image sequences. Both spatially-based and temporally-based artifacts are charted using this single metric. VIC is based on the velocital information in each image. A mathematical formulation for VIC is shown along with its relation to the spatial and temporal information content. Some strengths and weaknesses of the VIC formulation are discussed. VIC is tested on some standard image sequences with various spatio-temporal attributes. VIC is also tested on a standard image sequence with various degrees of blurring using a linear blurring algorithm. Additionally, VIC is tested using standard sequences that have been processed through a digital transmission algorithm. The transmission algorithm is based on the discrete cosine transform, and thus introduces many of the known digital artifacts such as blocking. Finally, the ability of VIC to chart image artifacts is compared to a few other traditional quality metrics. VIC offers a different role from traditional transmission-based quality metrics which require two images: the original input image and degraded output image to calculate the quality metric. VIC can detect artifacts from a single image sequence by charting variations from the norm. Therefore, VIC offers a metric for judging the quality of the image frames prior to transmission, without a transmission system or without any knowledge of the higher quality image input. The differences between VIC and transmission-oriented quality metrics, can provide a different role for VIC in analysis and image sequence processing.
A voting scheme for the design of composite filter is proposed here. Different types of synthetic discriminant function (SDF) filters, like minimum average correlation energy (MACE), minimum variance SDF (MVSDF), and optimal tradeoff SDF (OTSDF) have been proposed recently for the distortion-invariant recognition. Discretization of these filters is necessary to realize them using the available spatial light modulator (SLM), which limits the efficiency of the continuous domain filters. In this report, we address this SLM-constraint of the composite filter design. Our design starts with a binary SLM. In particular, binary modulation capability of the SLM is incorporated in the composite filter design as a constraint in the form of voting scheme nonlinearity.
A novel wavelet-based joint transform correlator (WJTC) for rotation-invariant pattern recognition and applications in optical image processing and remote sensing is investigated. First an optimal set of filter parameters and a mother wavelet filter are selected. These are used to extract features at different resolution from a set of rotationally distorted training images. Then a composite reference feature is formulated from these features for use in the WJTC. Simulation results for both noisy and noiseless environments are presented to verify the effectiveness of this technique.
Wavelet feature performance for the detection and recognition of targets from noisy images is investigated. Training patterns with different noise contents are first employed to come up with a statistical model for the dissimilarity of the reference target and noisy inputs. This model is then analyzed with Daubechies wavelet filter with extremal phase and vanishing moment. Simulation results show the potential of wavelet features that can be used in the decision making subsystem to yield high discrimination between target and non-target.
An innovative method that enhances detail in digital images by smoothing image pixels while introducing minimal distortion is described and tested. In particular, a 14 by 14 pixel region of a diital image is smoothed using a constrained Gaussian radial basis function method. This method centers on each pixel a Gaussian distribution of amplitude such that the sum of all distributions correctly reproduces the gray level of each pixel. To assess the method, the distortion of the smoothed image is measured by the deviation of its power spectrum, from that of the unsmoothed image, determined as a function of the Gaussian distribution width, and comparisons are made with bilinear interpolation, a conventional convolution smoothing technique. The new method is capable of removing more 'pixel noise' while introducing less image distortion, thus permitting the detection and examination of otherwise hidden detail in digital images. Examples include the detection and assessment of enemy weapons in military images and cancerous tumor medical images.
Wavelet based Joint transform correlator (JTC) for rotation invariant automatic target detection is proposed in this paper. Wavelet features of a set of rotationally distorted training images are first extracted at different levels of resolution. A simple composite filter formulation is then employed to construct the reference image of the JTC. Simulation results are presented showing the improved rotation-invariant detection performance of the proposed technique.
A polynomial-neural-network-based (PNN-based) path planning with an obstacle avoidance scheme is proposed for mobile robot navigation. The PNN is a feature-based mapping neural network which can be successfully trained to interpolate an unknown function by observing few samples. In this work, a very useful method of data analysis technique called the group method of data handling (GMDH) is used to build the PNN. The built PNNs are used for the path planning of a sonar sensor guided mobile robot. The major advantage of using the PNNs is to efficiently use the environment data and to reduce the computational complexity. Also, in this approach, no preprocessing of range data is required.
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