یافتن جایگزین بیت حداقل قابل توجه بهینه در تصویر پنهان با استراتژی برنامه ریزی پویا
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|24865||2003||13 صفحه PDF||سفارش دهید||7081 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Pattern Recognition, Volume 36, Issue 7, July 2003, Pages 1583–1595
The processing of simple least-significant-bit (LSB) substitution embeds the secret image in the least significant bits of the pixels in the host image. This processing may degrade the host image quality so significantly that grabbers can detect that there is something going on in the image that interests them. To overcome this drawback, an exhaustive least-significant-bit substitution scheme was proposed by Wang et al. but it takes huge computation time. Wang et al. then proposed another method that uses a genetic algorithm to search “approximate” optimal solutions and computation time is no longer so huge. In this paper, we shall use the dynamic programming strategy to get the optimal solution. The experimental results will show that our method consumes less computation time and also gets the optimal solution.
In the Internet, tons of various kinds of data are sent, transmitted and received every single moment. Some of them may be secret information of commerce and others confidential messages from the government, both of which are candidate preys for grabbers to access. In order to keep the grabbers away, a variety of techniques have been proposed. One of the most famous methods is data encryption  and , which uses a certain algorithm to transform data into cipher texts. Only the user that has keys can decrypt the secret data from the cipher texts. For any grabber who does not have a key, the cipher texts will look like nothing but streams of meaningless codes. Although data encryption is a good way to prevent grabbers from accessing secret data, it still has some weaknesses. The appearance of cipher texts would give grabbers an impulse to recover them. Moreover, grabbers might even simply destroy the cipher texts out of rage when they have trouble recovering them so that the legal receivers cannot get the data in time. That is the reason why data hiding  and  has been researched recently. Data hiding techniques embed the important data into multimedia data such as images, videos or movies. In this paper, we take an image for a carrier of secret data and name it as a host image. After embedding the secret data into the host image, the output image of this hiding process is called a stego- image. When we discuss image hiding, one of the most important things is that the quality of the stego-image must not be degraded too much after embedding. The goal of data hiding is to make the stego-image invisible to grabbers. Thus, if the process of embedding degrades the quality of the stego-image too much, any grabber will easily take notice of it. Techniques of least-significant-bit-based substitution are simple ones to achieve data hiding. A least-significant-bit-based substitution method replaces some least significant bits of the host image with the secret data. After least-significant-bit-based data hiding, the researches of data hiding focus on the difficulty of removing the embedded data through operations of images. Most researches concerned are concentrated upon watermarking ,  and ; the capacity of embedding is not the key point in those methods. However, in our opinion, one of the important things in data hiding is the embedding capacity. When we want to transmit important secret data via a certain technique of data hiding, it is obviously not practical if we have no choice but to break the secret data into pieces and put each piece in a large host image due to the limited capacity. On the other hand, the simple least-significant-bit substitution method directly replaces some least significant bits of each pixel value in the host image with the secret data; it does not focus on the difficulty of removing secret data through images operations but rather on the capacity of embedding. When talking about the capacity of embedding, the major concern is to make stego-image extremely hard for grabbers to sense the existence of the secret data. There exists one method to achieve this aim. The method first sets a bijective mapping function and transforms values of secret data into another set of values according to a bijective mapping function. It then replaces the rightmost least significant bits of each pixel in host image with transformed values to form the stego-image. It goes without saying that if we can find an optimal solution of bijective mapping function, we can use a bijective mapping function to make the stego-image have the best quality. In order to find the optimal solution of a bijective mapping function, the procedure of the exhaustive least-significant-bit substitution must exhaustively go through all possible bijective mapping functions. When the rightmost k least significant bits of the host image are replaced by the secret data, k being large, it takes huge computation time and makes the method impractical. In 2001, Wang et al.  proposed a method that uses a genetic algorithm to search for an approximate optimal solution of a bijective mapping function, and the computation time is no longer so huge as that of the exhaustive least-significant-bit substitution. Although Wang et al.'s method reduces the computation time, the solution it comes up with is not an optimal one of the bijective mapping function but an “approximate” optimal solution instead. In order to get an optimal solution of a bijective mapping function, we propose the method that uses a dynamic programming strategy to achieve this purpose. Our method not only gets an optimal solution of a bijective mapping function but also significantly reduces the computation time. The rest of this paper is organized as follows. We shall describe the related works first in Section 2. We shall introduce the image hiding in detail by using the least-significant-bit substitution in Section 2.1 and Wang et al.'s genetic algorithm in Section 2.2. After that, we shall describe our method in Section 3. Details of our method for finding an optimal solution are first introduced in Section 3.1, and then some properties about our solution are discussed in Section 3.2. In Section 4, the experimental results show how our method performs. Finally, the conclusions are presented in Section 5.
نتیجه گیری انگلیسی
Image hiding plays an important role in data transmission. It goes without saying that the computation time and quality of the stego-image are important research topics. The exhaustive least-significant-bit substitution method explores all the candidate substitution matrices to get the optimal solution. It becomes impractical when the number of replaced bits is large. Although the genetic algorithm keeps the computation time in a certain range, the solutions are merely near optimal. It directly influences the quality of the stego-images. In this paper, we proposed an optimal least-significant-bit substitution in image hiding using the dynamic programming strategy. Both the computation time and the quality of the stego-images are both better than the methods proposed in . The experimental results also demonstrate that our method gives quite an impressive performance.