Multi-Bit Data Hiding Scheme for Compressing Secret Messages

Abstract

The goal of data hiding techniques usually considers two issues, embedding capacity and image quality. Consequently, in order to achieve high embedding capacity and good image quality, a data hiding scheme combining run-length encoding (RLE) with multi-bit embedding is proposed in this paper. This work has three major contributions. First, the embedding capacity is increased 62% because the secret message is compressed before embedding into the cover image. Secondly, the proposed scheme keeps the multi-bit generalized exploiting modification direction (MGEMD) characteristics, which are effective to reduce modified pixels in the cover image and to maintain good stego image quality. Finally, the proposed scheme can prevent modern steganalysis methods, such as RS steganalysis and SPAM (subtractive pixel adjacency matrix), and is compared to MiPOD (minimizing the power of the optimal detector) scheme. From our simulation results and security discussions, we have the following results: First, there are no perceivable differences between the cover images and stego images from human inspection. For example, the average PSNR of stego images is about 44.61 dB when the secret message (80,000 bits) is embedded for test cover images (such as airplane, baboon, Lena) of size 512×512. Secondly, Appl. Sci. 2015, 5 1034 on average, 222,087 pixels were not modified after embedding for the cover image. That is to say, 12% less pixels are modified as compared to the MGEMD method. From the performance discussions, the proposed scheme achieves high embedding capacity and good image quality, but also maintains stego image security.

Data compression in ViSAR sensor networks using non-linear adaptive weighting

“…PSNR and SSIM definitions are seen in Eq. (17) and Eq. (18), respectively, for two entire 8-bit images x and y with the same size N 1 × N 2 .…”

“…We can also use the proposed approach for magnifying some multispectral images such as IKONOS and Quick-Bird images or images related to high-resolution optical remote sensing sensors [11][12][13]. In addition, there are many other applications for interpolation algorithms, e.g., data hiding [14][15][16][17][18], interpolation-based image denoising and demosaicking [19][20][21], SDTV to HDTV conversion (SD2HD) [2] in video processing, color processing [22], information fusion [8,9], and shadow detection [23] which can be assisted by ALMMSE algorithm. As we mentioned, the main focus of this research is towards interpolation-based image/video compression [10,24].…”

High-Capacity Image Steganography with Minimum Modified Bits Based on Data Mapping and LSB Substitution

“…Usually, in LSB-based methods, the secret data bits are substituted in k-least significant bits of each pixel. In the literature, various types of LSB-based techniques have been proposed [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23] targeted at high capacity, visual quality and security.…”

A Proposed Method for Image Steganography

“…Wings to the Research help to resist modern steganalysis attacks [9]. The all above discussed does not meet robustness up to the level.…”