A Second Order Derivatives based Approach for Steganography

TL;DR

This paper introduces a new steganography distortion function based on second order derivatives to better utilize image regions, aiming to improve undetectability by focusing on level curve analysis.

Contribution

It proposes a novel second order derivatives-based distortion function for steganography, with two implementation methods and promising initial experimental results.

Findings

Approaches are promising for steganography detection.

Second order derivatives effectively evaluate level curves.

New method improves feature preservation in noisy regions.

Abstract

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.