Pixel Value Differencing Steganography Research Articles

Multi-directional PVD steganography avoiding PDH and boundary issue

Pixel value differencing steganography (PVDS) technique efficiently identifies the edge and smooth regions from an image, therefore, the PVDS technique is more suitable for concealing the secret information in an image. Notwithstanding the advantages, the PVDS techniques give rise to some major issues. Most of the PVDS techniques suffer from boundary issue (BI). Further, the majority of the PVDS techniques are exposed to pixel difference histogram (PDH) analysis. In this paper, two improved PVDS based techniques, such as (1) indicator-based PVDS (IPVDS) and (2) multi-directional overlapped PVDS (MDOPVDS) has been proposed to address the BI as well as PDH issue. The IPVDS technique utilizes the concept of indicator pixel in a 2 × 3 pixel block. On the other hand, the MDOPVDS technique utilizes the pixel overlap strategy in a 3 × 3 pixel block using all three directions, such as (1) horizontal, (2) vertical, and (3) diagonal. Results of the proposed technique are evaluated in terms of the conflicting metrics like peak signal-to-noise ratio (PSNR), capacity, and security. Further, it is observed that the proposed technique maintains a good symmetry between the aforementioned conflicting metrics. Additionally, both the proposed technique successfully avoids the BI.

Statistical feature-based steganalysis for pixel-value differencing steganography

Pixel-value differencing (PVD) steganography is a popular spatial domain technology. Several PVD-based studies have proposed extended PVD steganography methods. The majority of these studies have verified their security against the regular-singular (RS) analysis. However, RS analysis is aimed at the feature of the least significant bit substitution method, which is relatively less significant for PVD steganography. The pixel difference histogram (PDH) is generally utilized to attack PVD steganography. If the embedding capacity is high, then the features on the PDH are evident; otherwise, the features are less obvious. In this paper, we propose a statistical feature-based steganalysis technique for the original PVD steganography. Experimental results demonstrate that, compared with existing steganalysis technique with weighted stego-image (WS) method, the proposed method effectively detects PVD steganography at low embedding ratios, such that there is no need of using the original embedding parameters. Furthermore, the accuracy and precision of the method are better than those of existing PVD steganalysis techniques. Therefore, the proposed method contributes to the security analysis of the original PVD steganography as an alternative to the commonly used RS, PDH and WS attack techniques.

Chi-square-based steganalysis method against modified pixel-value differencing steganography

Pixel-value differencing (PVD) steganography can embed a large quantity of information and effectively resist regular/singular (RS) detection. Recent steganography techniques that are based on PVD have only focused on improving embedding capacity and image quality. In terms of security, PVD steganography has typically only been tested against well-known RS detection analysis or pixel difference histogram attacks. Modified PVD (MPVD) is an improved method that eliminates certain detectable features of PVD, thereby increasing security. State-of-the-art studies in steganalysis have attempted to detect MPVD steganography by using features of the differences between cover images and stego images, and they have generated models to test suspicious images. However, these methods have low accuracy when the embedding capacity is low. In this study, we propose a chi-square goodness-of-fit-based steganalysis technique that can be applied to MPVD steganography. Experimental results demonstrate that the method effectively detects MPVD steganography even when the embedding ratio is low. Furthermore, the accuracy, precision, and implementation of the method are better than those of existing state-of-the-art techniques in detecting data hidden using MPVD steganography. Therefore, the proposed method contributes to the security analysis of PVD-extended steganography.

Digital Image Steganography Using Eight-Directional PVD against RS Analysis and PDH Analysis

The least significant bit (LSB) substitution techniques are detected by RS analysis and the traditional pixel value differencing (PVD) approaches are detected by pixel difference histogram (PDH) analysis. The PVD steganography can escape from PDH analysis by using the edges in multiple directions. This paper proposes a steganography technique by exploiting the edges in eight directions and also using LSB substitution to resist from both RS analysis and PDH analysis. For every 3×3 pixel block the central pixel is embedded with 3 or 4 bits of data by modified LSB substitution technique. Then this new value of the central pixel is utilized to calculate eight difference values with eight neighboring pixels. These eight difference values are used to hide the data. There are two types with regard to two different range tables. Type 1 uses 3 bit modified LSB substitution and range table 1. Type 2 uses 4 bit modified LSB substitution and range table 2. Type 1 and type 2 are also known as variant 1 and variant 2, respectively. Type 1 possesses higher PSNR and type 2 possesses higher hiding capacity.

Adaptive and Non-adaptive PVD Steganography Using Overlapped Pixel Blocks

There are two categories of steganography techniques using pixel value differencing (PVD), (1) adaptive range table PVD and (2) non-adaptive range table PVD. In adaptive PVD the quantization ranges are different for different pixels, but in non-adaptive PVD the range table is fixed for all the pixels. This paper proposes two PVD techniques with 1 $$\times $$ 2 pixel blocks in overlapped fashion. The first technique uses adaptive quantization range table and modular arithmetic for embedding and extraction. The second technique uses fixed quantization range table and addition/difference mechanism for embedding and extraction. The experimental results reveal that the proposed adaptive PVD technique possesses higher embedding capacity and PSNR as compared to the existing adaptive PVD techniques and the proposed non-adaptive PVD technique possesses higher embedding capacity as compared to the existing non-adaptive PVD techniques. Furthermore, it is also experimentally verified that the proposed techniques resist from RS analysis and pixel difference histogram (PDH) analysis.

A New Optimization Strategy for Solving the Fall-Off Boundary Value Problem in Pixel-Value Differencing Steganography

In Digital Image Steganography, Pixel-Value Differencing (PVD) methods use the difference between neighboring pixel values to determine the amount of data bits to be inserted. The main advantage of these methods is the size of input data that an image can hold. However, the fall-off boundary problem and the fall in error problem are persistent in many PVD steganographic methods. This results in an incorrect output image. To fix these issues, usually the pixel values are either somehow adjusted or simply not considered to carry part of the input data. In this paper, we enhance the Tri-way Pixel-Value Differencing method by finding an optimal pixel value for each pixel pair such that it carries the maximum input data possible without ignoring any pair and without yielding incorrect pixel values.

Adaptive PVD Steganography Using Horizontal, Vertical, and Diagonal Edges in Six-Pixel Blocks

The traditional pixel value differencing (PVD) steganographical schemes are easily detected by pixel difference histogram (PDH) analysis. This problem could be addressed by adding two tricks: (i) utilizing horizontal, vertical, and diagonal edges and (ii) using adaptive quantization ranges. This paper presents an adaptive PVD technique using 6-pixel blocks. There are two variants. The proposed adaptive PVD for 2×3-pixel blocks is known as variant 1, and the proposed adaptive PVD for 3×2-pixel blocks is known as variant 2. For every block in variant 1, the four corner pixels are used to hide data bits using the middle column pixels for detecting the horizontal and diagonal edges. Similarly, for every block in variant 2, the four corner pixels are used to hide data bits using the middle row pixels for detecting the vertical and diagonal edges. The quantization ranges are adaptive and are calculated using the correlation of the two middle column/row pixels with the four corner pixels. The technique performs better as compared to the existing adaptive PVD techniques by possessing higher hiding capacity and lesser distortion. Furthermore, it has been proven that the PDH steganalysis and RS steganalysis cannot detect this proposed technique.