Improvement of Universal Steganalysis Based on SPAM and Feature Optimization

The tendency for high-dimension of universal steganalysis characteristics toward intensifying, and lead to the rapid rise in complexity of algorithm in time and space domain. So maintain the level of detection rates, and reduce the dimension of features at the same time, have significance in research of steganalysis. This paper determines the optimal dimension of feature vectors by principal component analysis; using the concept of Fisher linear discriminant, with the degree of "aggregations within class" and "discreteness between classes" to evaluate the ability of each dimension features to distinguish natural and hidden carrier, and then select the optimal subset. The analysis directs at the mainstream universal steganalysis model-SPAM model, and the simulation results show that optimal subset has a good detection and low computational complexity.