  Steganography schemes are designed with the objective of minimizing a defined
distortion function. In most existing state of the art approaches, this
distortion function is based on image feature preservation. Since smooth
regions or clean edges define image core, even a small modification in these
areas largely modifies image features and is thus easily detectable. On the
contrary, textures, noisy or chaotic regions are so difficult to model that the
features having been modified inside these areas are similar to the initial
ones. These regions are characterized by disturbed level curves. This work
presents a new distortion function for steganography that is based on second
order derivatives, which are mathematical tools that usually evaluate level
curves. Two methods are explained to compute these partial derivatives and have
been completely implemented. The first experiments show that these approaches
are promising.
