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.
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.