... on the paper "Color
Image Steganalysis Based on Steerable Gaussian Filters Bank,"
Abdulrahman, Marc Chaumont, Philippe Montesinos, and Baptiste Magnier,
"Color Image Steganalysis Based on Steerable Gaussian Filters Bank," IH&MMSec'2016,
in Proceedings of the 4th ACM workshop on Information Hiding and
Multimedia Security, Vigo, Galicia, Spain, 6 pages, June 20-22, 2016.
Acceptance Rate = 36.2%. pdf. slides. Matlab
This page give additional information about the paper and the
technical part related to what we proposed.
About the dimension of the feature vector:
The first set, produced by , is made of
The second set is made of 4406 features and comes from SPAM features
- the gradient magnitude image of each channel (R, G, B) ; it gives
- the derivative image (related to the tangent vector) for each channel
G, B) ; it gives 1598 features.
More details about the
- The co-occurences matrices are computed with triplets which means
co-occurences have (2xT+1)^3 bins.
- 4 matrices are computed (horizontal right, and left, and vertical
and left) and then summed.
- 4 others matrices are computed (horizontal right, and left, and
right and left) and then summed.
- The two matrices are then concatenated in a feature vector.
- For each T values (except for the derivative image with T=3), the
vectors of each channel are
- For the gradient magnitude, T is equal to 2 or 3. This leads to a
dimension = 3x(2x5^3) + 3x(2x7^3) = 3x250 + 3x686 = 2808 features,
- For the derivative image, T is equal to 1, 2 or 3. This leads to a
dimension = 3x(2x3^3) + 3x(2x5^3) + (2x7^3) = 3x54+3x250+686 = 1598
for T=3, we sum the 3
co-occurrences matrices (from R, G, B channels) instead of concatenate
(indeed, otherwise, bins values are too small for T=3).