29,767 research outputs found
STEGANALYSIS WITH CLASSIFIER COMBINATIONS
ABSTRACT Blind steganalysis is based on choice of the feature set and the machine learning classifiers used for classification. While the performance of individual classifiers is good, the classification accuracy is seen to increase by appropriate combination of classifiers. This research has implemented image steganalysis with fusion of classifiers by various data fusion schemes. We intend to analyse the classification accuracy of fusion classifier under nine different fusion schemes. The chosen individual classifiers are Multi-Layer Perceptron (MLP) and Support Vector Machines (SVM). The feature set chosen for classification includes the calibrated, combined features of modified Discrete Cosine Transform (DCT) features and Markov features from a database of 1000 cover images and 1000 stego images. It has been identified that the classification accuracy of Decision template and Dempster-Shafer methods of fusion gives best results, while Bayes, average and sum fusion schemes give good results compared to the performance of individual classifiers
Fusion for Audio-Visual Laughter Detection
Laughter is a highly variable signal, and can express a spectrum of emotions. This makes the automatic detection of laughter a challenging but interesting task. We perform automatic laughter detection using audio-visual data from the AMI Meeting Corpus.
Audio-visual laughter detection is performed by combining (fusing) the results of a separate audio and video classifier on the decision level. The video-classifier uses features based on the principal components of 20 tracked facial points, for audio we use the commonly used PLP and RASTA-PLP features. Our results indicate that RASTA-PLP features outperform PLP features for laughter detection in audio.
We compared hidden Markov models (HMMs), Gaussian mixture models (GMMs) and support vector machines (SVM) based classifiers, and found that RASTA-PLP combined with a GMM resulted in the best performance for the audio modality. The video features classified using a SVM resulted in the best single-modality performance. Fusion on the decision-level resulted in laughter detection with a significantly better performance than single-modality classification
Fusing image representations for classification using support vector machines
In order to improve classification accuracy different image representations
are usually combined. This can be done by using two different fusing schemes.
In feature level fusion schemes, image representations are combined before the
classification process. In classifier fusion, the decisions taken separately
based on individual representations are fused to make a decision. In this paper
the main methods derived for both strategies are evaluated. Our experimental
results show that classifier fusion performs better. Specifically Bayes belief
integration is the best performing strategy for image classification task.Comment: Image and Vision Computing New Zealand, 2009. IVCNZ '09. 24th
International Conference, Wellington : Nouvelle-Z\'elande (2009
Machine Learning Methods for Attack Detection in the Smart Grid
Attack detection problems in the smart grid are posed as statistical learning
problems for different attack scenarios in which the measurements are observed
in batch or online settings. In this approach, machine learning algorithms are
used to classify measurements as being either secure or attacked. An attack
detection framework is provided to exploit any available prior knowledge about
the system and surmount constraints arising from the sparse structure of the
problem in the proposed approach. Well-known batch and online learning
algorithms (supervised and semi-supervised) are employed with decision and
feature level fusion to model the attack detection problem. The relationships
between statistical and geometric properties of attack vectors employed in the
attack scenarios and learning algorithms are analyzed to detect unobservable
attacks using statistical learning methods. The proposed algorithms are
examined on various IEEE test systems. Experimental analyses show that machine
learning algorithms can detect attacks with performances higher than the attack
detection algorithms which employ state vector estimation methods in the
proposed attack detection framework.Comment: 14 pages, 11 Figure
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