AUDIO STEGANALYSIS OF DSSS BASED ON STATISTICAL MOMENTS OF HISTOGRAM

Compared with the histogram of Discrete Cosine Transform (DCT) coefficients before the Direct Sequence Spread Spectrum (DSSS) embedding, the peak value of the histogram after the embedding decreases and expands toward the border. Based on the property, an audio steganalysis of DSSS based on statistical moments of histogram is proposed. The statistical moments of the histogram in DCT domain and its frequency domain and the statistical moments of the histogram of the wavelet coefficients of every level in frequency domain are calculated as the features of classification. Support Vector Machine (SVM) is exploited as the classifier. Experimental results show that the proposed technique is effective on the DSSS embedding in DCT domain using different embedding length, and the average detection rate is 91.75%.

Study on Dim Target Detection and Discrimination from Sea Clutter

Dim target detection from sea clutter is one of the difficult topics in ocean remote sensing application. By aiming at the shortcoming of false alarms when using track before detect （TBD） based on dynamic programming, a new discrimination method called statistics of direction histogram （SDH） is proposed, which is based on different features of trajectories between the true target and false one. Moreover, a new series of discrimination schemes of SDH and Local Extreme Value method （LEV） are studied and applied to simulate the actually measured radar data. The results show that the given discrimination is effective to reduce false alarms during dim targets detection.

An online anomaly detection method for stream data using isolation principle and statistic histogram

Online anomaly detection for stream data has been explored recently,where the detector is supposed to be able to perform an accurate and timely judgment for the upcoming observation.However,due to the inherent complex characteristics of stream data,such as quick generation,tremendous volume and dynamic evolution distribution,how to develop an effective online anomaly detection method is a challenge.The main objective of this paper is to propose an adaptive online anomaly detection method for stream data.This is achieved by combining isolation principle with online ensemble learning,which is then optimized by statistic histogram.Three main algorithms are developed,i.e.,online detector building algorithm,anomaly detecting algorithm and adaptive detector updating algorithm.To evaluate our proposed method,four massive datasets from the UCI machine learning repository recorded from real events were adopted.Extensive simulations based on these datasets show that our method is effective and robust against different scenarios.