In this paper, an effective method was introduced to steganography of text document in the host image. In the available steganography methods, the message has a random form. Therefore, the embedding capacity is generally low. In the proposed method, the main underlying idea was the sparse property of scanned documents. The scanned documents were converted from gray-level form to binary values by halftoning idea and then the information-included parts were extracted using the improved quadtree and separated from document context. Next, in order to compress the extracted parts, an algorithm was proposed based on reading the binary string bits, ignoring the zero behind the number, and converting them to decimal values. Embedding capacity of the proposed method is higher than that of other available methods with a random-based message. Therefore, the proposed method can be used in the secure and intangible transfer of text documents in the host image.
@cite_14 , a high capacity steganography was proposed that used a genetic algorithm to choose the suitable embedding order in a 4 @math 4 block. In fact, this paper indicated that conventional embedding order did not have the best image quality and embedding capacity. This method aimed to convert the best embedding order to an optimization problem and solve it using a genetic algorithm. Besides, this method determined the best LSB bits to have higher capacity. The embedding capacity was 3.95 bpp and the quality of embedded image was about 35 dB.
@cite_14
1992496874 1955882805 1966917092 2396805982
Abstract Until today, high-capacity is still one of important research parts in information hiding. After the secret information embedded is extracted, the demand for the image reversibility for the total recovery of the original object without any distortion goes high. This paper proposes a high capacity steganography using multilayer embedding (CRS), which can enhance the performance of information hiding system. The experimental results show the proposed CRS scheme has better performance than others. Moreover, the proposed CRS method can display the advantages of good quality image and low complexity of computation. This paper presents a complete methodology for designing practical and highly-undetectable stegosystems for real digital media. The main design principle is to minimize a suitably-defined distortion by means of efficient coding algorithm. The distortion is defined as a weighted difference of extended state-of-the-art feature vectors already used in steganalysis. This allows us to "preserve" the model used by steganalyst and thus be undetectable even for large payloads. This framework can be efficiently implemented even when the dimensionality of the feature set used by the embedder is larger than 107. The high dimensional model is necessary to avoid known security weaknesses. Although high-dimensional models might be problem in steganalysis, we explain, why they are acceptable in steganography. As an example, we introduce HUGO, a new embedding algorithm for spatial-domain digital images and we contrast its performance with LSB matching. On the BOWS2 image database and in contrast with LSB matching, HUGO allows the embedder to hide 7× longer message with the same level of security level. Abstract Steganography is knowledge and art of hiding secret data into information which is largely used in information security systems. Various methods have been proposed in the literature which most of them are not capable of both preventing visual degradation and providing a large embedding capacity. In this paper, we propose a tunable visual image quality and data lossless method in spatial domain based on a genetic algorithm (GA). The main idea of the proposed technique is modeling the steganography problem as a search and optimization problem. Experimental results, in comparison with other currently popular steganography techniques, demonstrate that the proposed algorithm not only achieves high embedding capacity but also enhances the PSNR of the stego image. In this paper, we construct and implement a new Steganography algorithm based on learning system to hide a large amount of information into color BMP image. We have used adaptive image filtering and adaptive non-uniform image segmentation with bits replacement on the appropriate pixels. These pixels are selected randomly rather than sequentially by using new concept defined by main cases with sub cases for each byte in one pixel. According to the steps of design, we have been concluded 16 main cases with their sub cases that covere all aspects of the input information into color bitmap image. High security layers have been proposed through four layers of security to make it difficult to break the encryption of the input information and confuse steganalysis too. Learning system has been introduces at the fourth layer of security through neural network. This layer is used to increase the difficulties of the statistical attacks. Our results against statistical and visual attacks are discussed before and after using the learning system and we make comparison with the previous Steganography algorithm. We show that our algorithm can embed efficiently a large amount of information that has been reached to 75 of the image size (replace 18 bits for each pixel as a maximum) with high quality of the output.