Adaptive Steganography Based on bargain Game

TL;DR

This paper introduces a novel adaptive steganography model using game theory to optimize the balance between embedding capacity and security across different domains, evaluated on standard images.

Contribution

It presents a new bargaining game-based approach for adaptive steganography that enhances security without sacrificing capacity in both spatial and transform domains.

Findings

Improved security-to-capacity ratio in steganography blocks.

Enhanced image quality as measured by PSNR and histogram analysis.

Validated on 2000 standard images showing superior embedding performance.

Abstract

The capacity and security of the confidential message on the channel are two important challenges in steganography. In this paper, a new block steganography model is presented using the bargain method so that a competitive model is introduced. In this game, the blocks are the same players. The bargain is provided with the aim of embedding information without reducing capacity as well as increasing security. The proposed model shows that it can be used both of the special domain and the transform domain, which are two important methods of steganography. For this purpose, an example of a special domain model is introduced in which, In the first step, the image is divided into $n \times n$ blocks, and in the second step using the graph coloring algorithm, pixels are considered to embed confidential information in each block. In the third step, regarding the bargaining method in game theory, each block plays the role of a player, that the competition between players is based on the defined goal function, and in the best blocks in terms of two criteria of capacity and security, which here means each block has a higher security-to-capacity ratio, so it has a higher priority, which is determined based on the bargaining model. Also, information embedded in LSB two bits. An example of a conversion domain method is also shows that security increases without decreasing in capacity. The conclusion is evaluated by three criteria: PSNR, histogram, and $\epsilon-secure$ also, 2000 standard images were evaluated and observed that the proposed method improves the block methods of embedding information.