Edge Adaptive Image Steganography Research Articles

A novel minimal distortion-based edge adaptive image steganography scheme using local complexity

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 of61 $\sim $ 65, proves to be robust against kurtosis and skewness distortion, resists histogram attack, RS steganalysis and high dimensional ensemble classifier at 80% block modifications.

Comparative Study of Three Steganographic Methods Using a Chaotic System and Their Universal Steganalysis Based on Three Feature Vectors.

In this paper, we firstly study the security enhancement of three steganographic methods by using a proposed chaotic system. The first method, namely the Enhanced Edge Adaptive Image Steganography Based on LSB Matching Revisited (EEALSBMR), is present in the spatial domain. The two other methods, the Enhanced Discrete Cosine Transform (EDCT) and Enhanced Discrete Wavelet transform (EDWT), are present in the frequency domain. The chaotic system is extremely robust and consists of a strong chaotic generator and a 2-D Cat map. Its main role is to secure the content of a message in case a message is detected. Secondly, three blind steganalysis methods, based on multi-resolution wavelet decomposition, are used to detect whether an embedded message is hidden in the tested image (stego image) or not (cover image). The steganalysis approach is based on the hypothesis that message-embedding schemes leave statistical evidence or structure in images that can be exploited for detection. The simulation results show that the Support Vector Machine (SVM) classifier and the Fisher Linear Discriminant (FLD) cannot distinguish between cover and stego images if the message size is smaller than 20% in the EEALSBMR steganographic method and if the message size is smaller than 15% in the EDCT steganographic method. However, SVM and FLD can distinguish between cover and stego images with reasonable accuracy in the EDWT steganographic method, irrespective of the message size.

LSB based non blind predictive edge adaptive image steganography

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.

Enhanced Chaos based Image Steganography using Edge Adaptive and Cat Mapping Techniques

is the science of hiding the messages in a medium. The existence of hidden messages remains imperceptible to the Intruders. The medium used for hiding the messages may be audio, video, text or images. The important goal of steganography is to protect the hidden message. There are many techniques to hide the payload in images. This will be achieved by applying different techniques in any medium. There are many techniques applied by the intruders to find the hidden information those techniques are called anti-steganalysis techniques. To provide the security to the payload after inserting the payload in to messages we need to do the anti steganalysis, if the hidden information is found then we need to provide the additional security. There is a need to build the system with highest security levels so that the anti steganalysis techniques can't find out the hidden information. In the proposed system there are certain areas which are suitable for hiding the payload are called Edges. Edges are good Regions of Interest or ROIs that are used for steganography. The proposed system uses edge adaptive image steganography (1) that uses the combinations of chaotic cat mapping (2) to provide additional security and matrix encoding (3) and LSBM (4) to embed the data in to image. The proposed mechanism guaranties the high imperceptibility and Fidelity which are the two important requirements for any Image steganography.

Parallel modular steganography using error images

AbstractThe embedding process for most of the state‐of‐the‐art adaptive steganographic methods (not easily detectable) such as highly undetectable steganography (HUGO), edge adaptive image steganography (EA), and Adaptive ±1 Steganography in Extended Noisy Regions, is contingent upon the neighboring pixel values to prevent discovery by steganalysis methods. Hence, the calculation of the possible embedding rate must be suspended until the embedding process is performed pixel by pixel, in sequence. If, however, the cover image is JPEG‐compressed, an error image can be considered as an embedding map implying where and to what extent secret bits can be embedded. In this paper, we present a modular steganography using error images. It is demonstrated that the detectability level of the resulting stego images is lower than that of state‐of‐the‐art schemes and the proposed method executes 2.32 times faster than HUGO. Further, to improve the time performance, a block‐wise parallel algorithm is presented where a single thread is assigned to each block of pixels. Because of the independent behavior of the embedding method and unlike the other methods, it is proved that each block of pixels can be embedded independently. The effectiveness of this parallel approach is analyzed and verified to execute approximately 55 times faster than serial execution. Copyright © 2014 John Wiley & Sons, Ltd.

Chaos based Edge Adaptive Image Steganography

Steganography is the science of hiding data into innocuous objects such that the existence of the hidden data remains imperceptible to an adversary. Steganography in images have varied techniques of implementation developed over time. Protection of the hidden information from an adversary is the most important goal of steganography and hence it is obvious that the security of a steganography system will increase if the payload remains illegible to an attacker even if he holds knowledge about the embedding method. It is also evident that certain areas in an image are more efficient for hiding data than the other parts of the image. These are called Regions of Interest or ROIs. Edge areas in an image are one of the ROIs that can be used for steganography. This paper proposes an edge adaptive image steganography mechanism which combines the benefits of matrix encoding and LSBM to embed data and also uses a chaotic mapping scheme to provide enhanced security to the payload. Efforts have been given to ensure that the proposed mechanism conforms to high Imperceptibility and Fidelity which are the essential quality requirements for any image steganography system.

Targeted Steganalysis of Edge Adaptive Image Steganography Based on LSB Matching Revisited Using B-Spline Fitting

In this letter, the authors point out that the readjusting phase of edge adaptive image steganography based on LSB matching revisited introduces a pulse distortion to the long exponential tail of the histogram of the absolute difference of the pixel pairs. Making use of this observation, a targeted steganalytic method based on B-Spline fitting is proposed. Experimental results show that the proposed method obtains excellent results for detecting stego images with low embedding rate. The dominant performance of our method compared with state-of-the-art blind steganalyzers, such as SPAM and SRM is apparent. Furthermore, our method can accurately estimate the threshold used in the secret data embedding procedure and can separate the stego images with unit block size from those with block sizes greater than one.

Edge Adaptive Image Steganography Based on LSB Matching Revisited

The least-significant-bit (LSB)-based approach is a popular type of steganographic algorithms in the spatial domain. However, we find that in most existing approaches, the choice of embedding positions within a cover image mainly depends on a pseudorandom number generator without considering the relationship between the image content itself and the size of the secret message. Thus the smooth/flat regions in the cover images will inevitably be contaminated after data hiding even at a low embedding rate, and this will lead to poor visual quality and low security based on our analysis and extensive experiments, especially for those images with many smooth regions. In this paper, we expand the LSB matching revisited image steganography and propose an edge adaptive scheme which can select the embedding regions according to the size of secret message and the difference between two consecutive pixels in the cover image. For lower embedding rates, only sharper edge regions are used while keeping the other smoother regions as they are. When the embedding rate increases, more edge regions can be released adaptively for data hiding by adjusting just a few parameters. The experimental results evaluated on 6000 natural images with three specific and four universal steganalytic algorithms show that the new scheme can enhance the security significantly compared with typical LSB-based approaches as well as their edge adaptive ones, such as pixel-value-differencing-based approaches, while preserving higher visual quality of stego images at the same time.