دانلود مقاله ISI انگلیسی شماره 25049
عنوان فارسی مقاله

تعبیه تعیین میزان مد آب دامنه تبدیل بهینه از طریق برنامه ریزی خطی

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
25049 2001 10 صفحه PDF سفارش دهید محاسبه نشده
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عنوان انگلیسی
Optimal transform domain watermark embedding via linear programming
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Signal Processing, Volume 81, Issue 6, June 2001, Pages 1251–1260

کلمات کلیدی
تعیین میزان مد آب - برنامه ریزی خطی - کپی رایت - موجک -
پیش نمایش مقاله
پیش نمایش مقاله تعبیه تعیین میزان مد آب دامنه تبدیل بهینه از طریق برنامه ریزی خطی

چکیده انگلیسی

Invisible digital watermarks have been proposed as a method for discouraging illicit copying and distribution of copyright material. In recent years, it has been recognized that embedding information in a transform domain leads to more robust watermarks. A major difficulty in watermarking in a transform domain lies in the fact that constraints on the allowable distortion at any pixel may be specified in the spatial domain. The central contribution of the paper is the proposal of an approach which takes into account spatial domain constraints in an optimal fashion. The main idea is to structure the watermark embedding as a linear programming problem in which we wish to maximize the strength of the watermark subject to a set of linear constraints on the pixel distortions as determined by a masking function. We consider the special cases of embedding in the DCT domain and wavelet domain using the Haar wavelet and Daubechies 4-tap filter in conjunction with a masking function based on a non-stationary Gaussian model, but the algorithm is applicable to any combination of transform and masking functions. Our results indicate that the proposed approach performs well against lossy compression such as JPEG and other types of filtering which do not change the geometry of the image.

مقدمه انگلیسی

The idea of using a robust digital watermark to detect and trace copyright violations has stimulated significant interest among artists and publishers in recent years. Podilchuk [17] gives three important requirements for an effective watermarking scheme: transparency, robustness and capacity. Transparency refers to the fact that we would like the watermark to be invisible. The watermark should also be robust against a variety of possible attacks by pirates. These include robustness against compression such as JPEG, scaling and aspect ratio changes, rotation, cropping, row and column removal, addition of noise, filtering, cryptographic and statistical attacks, as well as insertion of other watermarks [15]. The other requirement is that the watermark be able to carry a certain amount of information i.e. capacity. In order to attach a unique identifier to each buyer of an image, a typical watermark should be able to carry at least 60– of information. However, most of the work in watermarking has involved a one bit watermark. That is, at detection a binary decision is made as to the presence of the watermark most often using hypothesis testing [23]. Barni [1] encodes roughly by embedding 1 watermark from a set of 1000 into the DCT domain. The recovered watermark is the one which yields the best detector response. Watermarking methods can be divided into two broad categories: spatial domain methods such as [4] and [16] and transform domain methods. Transform domain methods have for the most part focused on DCT [17], [1] and [14], DFT [13] and [2] and most recently wavelet domain methods [17], [3] and [24]. Transform domain methods have several advantages over spatial domain methods. Firstly, it has been observed that in order for watermarks to be robust, they must be inserted into the perceptually significant parts of an image. For images these are the lower frequencies which can be marked directly if a transform domain approach is adopted [6]. Secondly, since compression algorithms operate in the frequency domain (for example DCT for JPEG and wavelet for EZW) it is possible to optimize methods against compression algorithms as will be seen in Section 3. Thirdly, certain transforms are intrinsically robust to certain transformations. For example, the DFT domain has been successfully adopted in algorithms which attempt to recover watermarks from images which have undergone affine transformations [13]. While transform domain watermarking clearly offers benefits, in some cases it is desirable to specify constraints in another domain (spatial or another transform domain). In this case, the problem is more challenging since it is more difficult to generate watermarks in one domain while taking into account constraints in another. For example, the problem arises since constraints on the acceptable level of distortion for a given pixel may be specified in the spatial domain. In the bulk of the literature on adaptive transform domain watermarks, a watermark is generated in the transform domain and then the inverse transform is applied to generate the spatial domain counterpart. The watermark is then modulated as a function of a spatial domain mask in order to render it invisible. However, this spatial domain modulation is suboptimal since it changes the original frequency domain watermark. In the case of a DFT domain watermark, multiplication by a mask in the spatial domain corresponds to convolution of the magnitude of the spectrum. Unfortunately, to correctly account for the effects of the mask at decoding a deconvolution problem would have to be solved. This is known to be difficult and to our knowledge in the context of watermarking this problem has not been addressed. Methods proposed in the literature simply ignore the effects of the mask at decoding. One alternative which has recently appeared is the attempt at specifying the mask directly in the transform domain and ignoring spatial domain masking [17]. However other authors (e.g. Swanson [20]) have noted the importance of masking in the spatial domain even after a frequency domain mask has been applied. It should be noted that masking in one domain is not easily formulated since defining a spatial mask influences a frequency mask and vice-versa. In this publication, we develop a new approach for the mathematical modelling of the embedding process. In particular, we derive an optimized strategy for embedding a watermark in the wavelet and DCT domains when the masking constraints are specified in the spatial domain. In fact, the key idea is to optimize the encoding of the watermark with respect to the detector while using all available information about the image. This framework overcomes the problems with many proposed algorithms which adopt a suboptimal spatial domain truncation and modulation as determined by masking constraints. Furthermore, we will develop an algorithm which is image dependent. Unlike many of the embedding strategies described in the literature which treat the image as noise possibly modelled by a probability distribution, the algorithm we describe uses information about the image at embedding. We consider only the problem of generating watermarks which are robust against attacks that do not change the geometry of the image. We will work with an watermark which corresponds to a capacity sufficient for most watermarking applications. We begin in Section 2 by presenting the spatial domain masking methods we adopt in the rest of the paper. In Section 3, the embedding algorithm is described and applied to the case of DCT domain embedding. Then, in Section 4, we derive a new channel coding strategy which greatly improves the performance of the underlying algorithm. In Section 5, we show how the algorithm can be applied in the wavelet domain. In Section 6, we present our results and a comparison of the DCT and wavelet domain algorithms followed by the conclusion in Section 7.

نتیجه گیری انگلیسی

In this article, we have described a new mathematical model which describes the process of embedding a watermark in a transform domain when the masking constraints are specified in the spatial domain. This model has five characteristics which make it extremely appealing: (1) The algorithm is extremely flexible in that constraints as determined by masking functions can be easily incorporated in the spatial domain and any linear transform domain may be used although here we considered the special cases of the Haar and Daubechies wavelets as well as DCT domain embedding. Also, extra constraints may be added in the frequency domain. (2) We show how to handle problems with truncation in an optimal way and propose the novel approach of modifying all coefficients even though we are only interested in a subset. (3) The algorithms resist well against JPEG compression and we observe in particular that matching the embedding domain with the compression domain and incorporating JPEG quantization tables at the embedding stage leads to considerable gains. (4) The algorithm generates a non-additive and image dependent watermark which resists the watermark copy attack [10]. (5) At the embedding stage the image is not treated as noise which is an important property of the most robust watermarking schemes as noted by Cox [7]. In fact the algorithm uses available information about the image at the embedding stage to maximize the decoder response. Table options While much has been accomplished by structuring the problem of watermarking within this framework, many new research directions arise. We note five possiblities in particular: (1) While the DCT domain algorithm resists well against JPEG compression further research is needed in order to adapt the wavelet domain approach so that it is resistant against EZW and SPIHT compression. (2) Work is currently also under way to apply the ideas of [13] so as to make the algorithm resistant to geometric changes as well. (3) Another topic of further research is the incorporation of more sophisticated spatial domain masks. Most of the masks proposed in the watermarking literature model texture, luminance and/or frequency. Osberger [12] however identifies several higher order factors which have been used to weight distortion metrics (typically the distortion produced by compression algorithms). The most important factors are: • Contrast. Regions which have a high contrast with their surrounds attract our attention and are likely to be of greater visual importance. • Size. Larger regions attract our attention more than smaller ones however a saturation point exists after which the importance due to size levels off. • Shape. Regions which are long and thin (such as edges) attract more attention than round flat regions. • Colour. Some particular colours (red) attract more attention. Further more the effect is more pronounced when the colour of a region is distinct from that of the background. • Location. Humans typically attach more importance to the center of an image. • Foreground/background. Viewers are more interested in objects in the foreground than those in the background. • People. Many studies have shown that we are drawn to focus on people in a scene and in particular their faces, eyes, mouth and hands. We note that these factors are specified in the spatial domain and not easily converted to the frequency domain. Further, work could involve incorporating these elements in the attempt to generate more accurate spatial domain masks although detection of these elements is difficult to automate. (4) While some work has been done in capacity (e.g. [18]) the bulk of the results concern additive watermarks. An interesting topic of further research is the calculation of the capacity of the proposed non-additive scheme.

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