A novel minimal distortion-based edge adaptive image steganography scheme using local complexity
- PDF / 3,129,371 Bytes
- 24 Pages / 439.642 x 666.49 pts Page_size
- 46 Downloads / 219 Views
A novel minimal distortion-based edge adaptive image steganography scheme using local complexity (BEASS) Debina Laishram1 · Themrichon Tuithung1 Received: 11 January 2020 / Revised: 23 July 2020 / Accepted: 31 July 2020 / © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract The advantage of spatial domain image steganography techniques is their capacity to embed high payloads of data by directly modifying image pixels. While these techniques have a high-embedding capacity, they often create visual and statistical distortion in smoother regions. Most existing edge steganography techniques divide an image into blocks and insert data by processing the blocks in a linear order, but these method also has multiple drawbacks. First, if the selected block has an insufficient number of edge pixels, it may result in multiple blocks being processed. Second, at high embedding rates, the method creates severe distortion as multiple message bits are hidden in edge pixels and surrounding non-edge pixels without analyzing the statistical dependencies and correlation of pixels, compromising data security. The aim of the proposed method is to construct a Block-wise Edge Adaptive Steganography Scheme (BEASS) using textured regions, particularly edges and surrounding pixels. This scheme dynamically chooses the region to embed messages using a local complexity measure of Standard Deviation. It offers high payload, minimal distortion embedding by hiding three message bits into edge pixels using the minimal Mean Square Error to determine the embedding capacity of neighboring non-edge pixels within the block to preserve the statistical dependencies. The practical merit of this approach was validated and compared with existing algorithms, and experimental results find that the proposed method surpasses IQM tests, achieves a high PSNR of 61∼65, proves to be robust against kurtosis and skewness distortion, resists histogram attack, RS steganalysis and high dimensional ensemble classifier at 80% block modifications. Keywords Adaptive image steganography · Image Quality Metrics (IQM) · Least Significant Bit (LSB) · Local complexity analysis · Statistical distortion · Mean Square Error (MSE) · Steganalysis
Debina Laishram
[email protected] 1
Department of Computer Science and Engineering National Institute of Technology, Nagaland, India
Multimedia Tools and Applications
1 Introduction Image steganography is the art and science of covert communication in digital images. Generally, the image in which we intend to embed secret data is called the cover image, and the resultant image after data is embedded is called a stego image. The design of any steganographic scheme should adhere to three principal requirements: first, ensure high embedding capacity; second, maintain high imperceptibility; and third, provide maximal robustness against steganalytic attacks attempting to the break the scheme. Unfortunately with increase in payload, visual and statistical distortion in the form of noise will be introduced in
Data Loading...
