A Second Order Derivatives based Approach for Steganography
This work addresses the challenge of improving steganography for secure data hiding by focusing on less detectable modifications in noisy or chaotic image regions, though it appears incremental as it builds on existing distortion function concepts.
The authors tackled the problem of designing a distortion function for steganography by proposing a new approach based on second order derivatives to evaluate level curves in images, with initial experiments indicating promising results.
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.