LSB Based Non Blind Predictive Edge Adaptive Image Steganography

TL;DR

This paper introduces a high-capacity edge-adaptive image steganography method that uses a predictive approach to embed data in selected image areas, achieving improved capacity and security with minimal distortion.

Contribution

It proposes a novel Predictive Edge Adaptive steganography technique using MMED predictor for higher embedding capacity and lower distortion compared to existing methods.

Findings

Achieves higher embedding capacity than existing schemes.

Maintains lower distortion levels during data embedding.

Demonstrates improved security against detection.

Abstract

Image steganography is the art of hiding secret message in grayscale or color images. Easy detection of secret message for any state-of-art image steganography can break the stego system. To prevent the breakdown of the stego system data is embedded in the selected area of an image which reduces the probability of detection. Most of the existing adaptive image steganography techniques achieve low embedding capacity. In this paper a high capacity Predictive Edge Adaptive image steganography technique is proposed where selective area of cover image is predicted using Modified Median Edge Detector (MMED) predictor to embed the binary payload (data). The cover image used to embed the payload is a grayscale image. Experimental results show that the proposed scheme achieves better embedding capacity with minimum level of distortion and higher level of security. The proposed scheme is compared with the existing image steganography schemes. Results show that the proposed scheme achieves better embedding rate with lower level of distortion.