Image Steganalysis using Feature Selection based on Mutual Information and Adaptive Particle Swarm Optimization - Kaur, Jasmanpreet (My University, 2018)
In recent years, Steganalysis has been an area of active research. Feature Selection is a necessary phase of steganalysis in order to achieve high detection accuracy. Steganalytic feature selection methods based on Minimal Information (MI) and Adaptive Weight based Particle Swarm Optimization (APSO) are proposed in this work in order to eﬀectively reduce the high space dimensionality of the sta tistical features used in state-of-the-art steganalysis. First the diﬀerentiability of each feature dimension is calculated using MI parameter and further weight based sorting is carried out for the whole feature dimensions. The features sorted in descending order of diﬀerentiability among another features. So ﬁrst few feature dimensions are selected in this phase. In order to further optimize the feature reduction and to reduce inseparable features, weight adaptive PSO in used which further reduces the feature vector space to a threshold value decided. The ﬁtness function used to get the best particles in the PSO is AUC measure of a classiﬁer which is calculated for each particle and the particle having high AUC is chosen as ﬁnal optimal selected features. The eﬀectiveness of the proposed feature selection is carried out by classifying the combined dataset of stego and cover images taken from BOSSbase dataset using three diﬀerent machine learning techniques named as decision tress, kNN and SVM. The statistical features used for evaluation are SPAM and CC-PEV feature extraction methods which have 686 and 548 feature space dimensionality. The ﬁnal feature reduction is tested at 80 and 100 selected features. Experimental results shows that high classiﬁcation accuracy is achieved with the proposed features reduction methods.
Master of Engineering- Computer Science
Keywords: Steganalysis; Area Under Curve; Feature Selection; Mutual Information; PSO