Figure 3 The bits extraction framework based on two-dimensional quantization and coding, where D denotes the number of features; K denotes the number of feature pairs; c k denotes the feature index for the kth feature pair (k=1,…,K); s k denotes the corresponding quantized bits.
Figure 6 enumerates the ordered mutual information between each feature and identities, where the horizontal axis presents the mutual information and the vertical axis denotes the feature names.
In Fig. 5, a horizontal axis denotes the feature value and a vertical axis denotes membership belonging to a positive group.
where v i denotes the feature vector of the i t h training sample and the permittivity ε ri is the corresponding target value.
Figure 5 enumerates the ordered mutual information between each feature and activity types, where the horizontal axis presents the mutual information and the vertical axis denotes the feature names.
As aforementioned, in LOR framework, for a given target-compound pair (T i, C ij ) a feature vector C ij = ∅(T i, C ij ) should be defined, where ∅ denotes the feature function.
In the case of steganalysis as performed in this article, denotes the feature vector corresponding to image, while is the corresponding class of the image (i.e., stego or cover).
Twelve sensitive features are selected from the entire feature set using the DET method with a given threshold, Thr = mean(α j ), and eight sensitive features are selected using the IHFST method with a given threshold, Thr = mean(β j ), where j = 1, 2,…, 32 denotes the feature index.
Let c i d d be the fixed point in int i d d to which every feature element v i d j d d that falls within the interval has to be mapped, where i d ∈{0,1, ⋯ S d - 1} denotes the interval index and j d ∈ {1,2 ⋯} denotes the feature element index.
