اطلاعات پنهان در تصاویر خاکستری توسط برنامه ریزی پویا بر اساس یک مدل بصری انسان

A new method for data hiding in grayscale images based on a human vision model with distortion-minimizing capabilities is proposed. Each of the eight bit planes of an input grayscale image is viewed as a binary image, into which message data are embedded horizontally. Two optimization techniques, namely, block pattern coding and dynamic programming, are proposed for image distortion minimization. Experimental results show good performs of the proposed method.

Data hiding in images is a useful technique for secret communication. Many data hiding techniques have been proposed recently [1], [2] and [3]. A common approach is least-significant-bit (LSB) replacement, which embeds message data in the LSB planes of an image. The image into which a message is hidden is called a cover image, and the result a stego-image. Wang et al. [4] embedded a binary image in the fifth LSB plane of a cover image using a genetic algorithm and a local pixel adjustment method to lower the distortion in the stego-image. Chang et al. [5] used dynamic programming to obtain an optimal solution for the LSB substitution method. Chan and Cheng [6] and [7] presented an optimal pixel adjustment process to improve the quality of the stego-image acquired by Wang's schemes. Thien and Lin [8] embedded data in images digit by digit using a modulus function, which improves LSB substitution not only in eliminating false contours but also in reducing image distortion. Lee and Chen [9] applied variable-sized LSB insertion to estimate the maximum embedding capacity by a human visual system (HVS) property, and to maintain image fidelity by removing false contours in smooth image regions. Liu et al. [10] presented a novel bit plane-wise data hiding scheme using variable-depth LSB substitution and employed post-processing to eliminate the resulting noticeable artifacts. Most of the above methods lack consideration of using precise human visual models in improving the data hiding effect. Instead, Wu and Tsai [11] presented a method based on the HVS by modifying quantization scales according to variation insensitivity from smooth to contrastive to improve stego-image quality. And Lie and Chang [12] presented an adjusted LSB technique with the number of LSBs adapting to the pixels of different grayscales. On the other hand, some steganalysis techniques were developed to detect secret messages among stego-images. Lyu and Farid [13] developed a universal blind detection scheme to detect hidden messages in stego-images, which uses wavelet-like decomposition to build higher-order statistical models of natural images and adopts the support vector machine as an optimal classifier to separate stego-images from cover images. The method demonstrates good performance on JPEG images and the selected statistics is rich enough to detect hidden data in the results yielded by a very wide range of steganographic methods. In addition, to detect data hidden in LSBs in the spatial domain, it is observed that the basic LSB substitution method changes pixel values only between 2i and 2i+1 in the i-th bit plane of the pixel value. This leads to an effective steganalytic technique, the RS method proposed by Fridrich et al. [14], which not only can expose the presence of secret data but also can estimate the length of the embedded data. In this study we propose a method to embed data into a grayscale image, based on the use of a new HVS model to estimate the number of usable bits of each pixel in the cover image. Furthermore, a block pattern encoding method is proposed to embed up to three data bits in a 2×2 block of the bit planes without yielding visible image quality degrading. This is achieved by using two optimization techniques. The first technique utilizes multiple block pattern encoding tables, from which an optimal one is chosen for each input image; and the second technique uses dynamic programming to divide the message data stream into appropriate bit segments for optimal data bit embedding in the image blocks to minimize a cost function. The proposed method can extract embedded data without referencing the original image. In the remainder of this paper, we introduce the idea behind the proposed method in Section 2. In Section 3, we describe the adopted HVS model and the corresponding cost function. In Section 4, the proposed data hiding method is described. The corresponding data recovery process is proposed in Section 5. Some experimental results are given in Section 6, followed by discussions and conclusions in Section 7.

A data hiding method for hiding messages into grayscale images with distortion reduction effects have been proposed. Two novel techniques for reducing distortions in stego-images have been adopted, one being an optimal dynamic programming algorithm, and the other the use of multiple block pattern encoding tables. First, a cost function has been proposed to estimate the weight of each bit in each pixel to be replaced according to an HVS model. Next, a horizontal data hiding scheme in which message data are embedded in a sequence of bit planes has also been proposed to decrease possible distortions in stego-images. Also, an optimal block pattern encoding table is chosen from 128 alternative ones for use in data embedding to minimize image distortion. The encoding tables are designed in such a way that up to three bits in a 2×2 image block can be embedded. Finally, the proposed method minimizes further the distortion using dynamic programming based on the proposed cost function. The proposed dynamic programming algorithm has quadratic space and time complexities, and so takes long time to embed a long secret message. For applications with concerns of distortion reduction, the proposed method is good to use. If computation speedup is desired, the proposed greedy search algorithm may be applied. If high-speed processing is necessary, our method can be adapted to run on a parallel computer with each of the 128 block pattern encoding tables processed separately, and the dynamic programming steps parallelized. At least two approaches may be adopted to make the proposed method more robust. First, multiple copies of a secret message may be embedded in the input image randomly with control by a key, so that an attack will not entirely destroy the secret information. And after the data are extracted by the proposed method, we may apply a voting scheme to recover the secret. The second approach is to try to place secret data in the more-significant-bits of the cover image, for example, in bp2 and bp3 in the proposed method, assuming that most attacks to BMP images are conducted to the LSBs. Because the information encoded in these bit-planes cannot be removed in most applications (otherwise, the image will be seriously distorted or destructed), hopefully this method will work in real applications. Future works may be directed to extending the proposed method to process blocks larger than 2×2, resulting possibly in greater reduction of image distortion. It is also possible to embed multiple message data in a grayscale image for protecting the intellectual property right and authenticating multimedia data, to define more general cost functions for other HVS models, and to design better encoding tables to reduce image distortion further.