Chapter 1 Introduction
1.5 Thesis Organization
The remainder of this thesis is organized as follows. In Chapter 2, we review the related works about the techniques of data hiding and the creation of art images. In Chapter 3, the proposed method for creation of line-based Cubism-like images and the application of it to covert communication by invisible reversible pixel re-coloring are described. Similarly, we introduce the proposed method for creation of strip-based Futurism-like images and covert communication by variable sub-region coloring via such images in Chapter 4. In Chapter 5, the proposed method for creation of rectangle-based Neo-Plasticism-like images and the technique of information hiding via such images by building the binary partition tree and re-coloring the rectangular regions are described. Finally, conclusions of our study and suggestions for future works are given in Chapter 6.
13
Chapter 2
Review of Related Works
2.1 Previous Studies on Creations and Applications of Computer Art
Images
In recent years, the topic of creating art images via the use of computers often arouses interests of people. More and more researchers investigate the problem of how to combine the computer technology and the art image creation for various applications, from semi-automatically to automatically. With the increasing maturation of computer technologies, creating art image automatically is the main development goal nowadays. Hertzmann [1] surveys many ideas of creating art images by stroke-based rendering (SBR) which is defined to be an automatic approach to creating non-photorealistic imagery by placing discrete elements like paint strokes and stipples. He also surveyed several SBR algorithms and styles such as painting, pen-and-ink drawing, tile mosaics, and so on. The common goal of these image styles is to make art images look like some other types of images. For example, two images created by watercolor painting and oil painting in Hertzmann [2] and Hertzmann [3], respectively, are shown in Figure 2.1. Some other types of art images are shown in Figure 2.2, where Figure 2.2(a) is an image created by pen-and-ink illustration proposed by Salisbury [4], Figure 2.2(b) is a stipple image via a stipple placement method proposed by Mould [5], and Figure 2.3(c) shows a stain-glass
14
image created by an image filter presented in Mould [6].
(a)
(b)
Figure 2.1 The images created by Hertzmann [2] and Hertzmann [3]. (a) An image with the effect of watercolor painting. (b) An image with the effect of oil painting.
(a) (b) (c)
Figure 2.2 Other types of art images. (a) A pen-and-ink drawing from Salisbury [4]. (b) A stipple image from Mould [5]. (c) A stained glass image from Mould [6].
Moreover, another type of art images is mosaic image. Mosaic images are the art of creating works, each being composed of small shapes, such as squares, circles,
15
triangles, and so on. Different from the fixed direction of mosaic arrangement, Hausner [7] creates a tile mosaic image by placing tiles to follow the edges to make the image smoother. Figure 2.3 shows some examples from Hausner [7].
Another important criterion for art image creation is to limit the number of strokes so that the resulting image looks like an abstract painting, such as the images shown in Figure 2.4 which come from Haeberli [8]. Besides, Song, et al. [9] produces an abstract synthetic art by fitting shapes like triangles or rectangles to regions in segmented images, as shown in Figure 2.5.
Figure 2.3 Some tile mosaic images created by Hausner [7].
Figure 2.4 Images created by Haeberli’s method [8].
16
In this study, we will focus on creating abstract-type images. An abstract-type image does not show a good match to the source image; however, it emphasizes the global trend or distribution of the image. The result will not look like a painting, but keep some properties of the original image. Furthermore, we try to combine some styles of Western art to create our computer art, like the use of the line feature in the Cubism, Futurism, and Neo-Plasticism schools. As a result, three different abstract-type line-dominated art images are generated in this study, namely, line-based Cubism-like image, strip-based Futurism-like image, and rectangle-based Neo-Plasticism-like image. The detailed descriptions of the creation processes for these types of images will be described in subsequent chapters.
2.2 General Review on Information Hiding Techniques
Information hiding is a technique which embeds data imperceptibly into cover images, so that people will not perceive the existence of the hidden data. Many
Figure 2.5 Images created by Song, et al. [9].
17
information hiding techniques have been proposed for various purposes such as covert communication, authentication, or steganography. Moreover, information hiding techniques often utilize the weaknesses of human visual system. A well-known method is least significant bit (LSB) modification which changes the LSBs of the pixels of an image to embed information. For instance, Chan and Cheng [10]
presented a data hiding method by simple LSB substitution, and Wu and Tsai [11]
proposed an information hiding method according to a human vision model. They hid the secret messages in the smooth areas of an image based on the characteristics of human vision, so that the image can arouse no notice from observers.
On the other hand, the topic of data hiding via images can be classified into three groups, namely, the spatial-domain method, the frequency-domain method, and the combination of them [12]. Generally, a method in the spatial domain is sensitive against attacks like compression, but its implementation is simple. In the frequency domain, a hiding technique overcomes the problem related to robustness found in the spatial domain, but sometimes produces more distortion. For example, Ni, et al. [13]
presented a reversible data hiding algorithm for embedding data in the spatial domain by using the zero or the minimum point of the histogram and slightly modifying the pixel values. Xuan, et al. [14] proposed an approach to hiding secret data into one (or more) middle bitplane(s) of the integer wavelet transform coefficients in the middle and high subbands of the frequency domain. No matter what types they belong to, most of these researches are based on pixel-wise or block-wise operations and make use of few image features.
In this study, data hiding methods using individual features of art images will be proposed. Unlike the traditional methods of data hiding via images, we will hide data in the creation process of art images by modifying the average RGB value of each region of an image or by building the structure of a partition tree. More than this, we
18
will also implement some schemes to enhance the security of hiding by randomizing the direction of tree building or the order of coloring. In the following chapters, the details will be described.
2.3 Previous Studies on Information Hiding Techniques via Art Images
The combination of information hiding techniques and art image creation is a new idea of information security technology. Techniques based on this idea utilize the characteristics of the creation process of the art image to embed extra information in the generated images. Due to this way of camouflage, secret data can so be kept or transmitted covertly and securely. In addition, hackers will also tend to get unaware of the secret embedded in such images and this reduces the danger of being stolen or being tampered with.
Specifically, Lin and Tsai [15] proposed algorithms to embed secret messages in image mosaics by adding visible boundary regions to the four sides of tiles and modifying the histogram of tile images. Wang and Tsai [16] presented a data hiding technique for image mosaics as well. By varying the overlapping degrees of adjacent tile images, the method can create a new-style mosaic image in which bits of the message data are embedded. Some resulting images created via these two methods are shown in Figure 2.6. Different from the intuitive idea of image mosaics, Lai and Tsai [17] created a new type of mosaic image, called secret-fragment-visible mosaic, which is reconstituted with rectangle fragments yielded by partitioning of the original image. A method to embed secret messages is proposed by switching the relative positions of tile images which have similar colors in an identical bin of the histogram.
The resulting mosaic image is still a meaningful image like another one, as shown in
19
Figure 2.7.
In addition to previous methods, numerous researches on combining other types of art images and data hiding have been given. Hsu and Tsai [18] presented three new types of art images and three methods to hide secret information in art images by using the features of the creation process. The first type of image, digital puzzle image, is generated to embed data by modifying the orientations, sizes, and angles of the
(a) (b)
Figure 2.6 Image mosaics. (a) An image mosaic created from Lin and Tsai [15]. (b) An image mosaic created from Wang and Tsai [16].
(a) (b)
Figure 2.7 A Secret-fragment-visible mosaic image created with Lai and Tsai’ method [17]. (a) Original image. (b) Generated secret-fragment-visible mosaic image.
20
puzzle pieces. Second, in the new type of pointillistic image, palette colors are used for data hiding by varying the RGB values of each color dot of the pointillistic image.
And the last, a new art image called circular-dotted image is created to embed secret messages by using the drawing order of the circular dots and a circular dot overlapping scheme. Some examples of the art images created by Hsu and Tsai [18]
are shown in Figure 2.8.
Additionally, an information hiding approach was proposed through the use of stained glass images by Hung and Tsai [19]. The secret data can be hidden in stained glass images by modifying the tree structure used in the creation process. A result generated by the method is shown in Figure 2.9(a). Chang and Tsai [20] created a new type of art image, called tetromino-based mosaic, which is composed of tetrominoes of the Tetris game. A tetromino is a geometric shape composed of four squares which is connected orthogonally. By the composition of geometric forms, tetrominoes can be combined to fit into a fixed shape (rectangles mostly) to form blocks which then are used to fill an image plane. A data hiding method is proposed by using distinct
(a) (b) (c)
Figure 2.8 Art images created by Hsu and Tsai [18]. (a) A digital puzzle image. (b) A digital pointillistic image. (c) A digital circular-dotted image.
21
combinations and color shifting of the tetromino elements. An image yielded by Chang and Tsai [20] is shown in Figure 2.9(b).
In this study, we also propose new methods which combine information hiding techniques and art image creation to achieve covert communication. By utilizing the characteristics of the creation processes of three art images, which are line-based Cubism-like image, strip-based Futurism-like image, and rectangle-based Neo-Plasticism-like image, the images can be transmitted or kept with the secret data embedded without arousing attention from other people.
(a) (b)
Figure 2.9 Two examples of art images. (a) A stained glass image from Hung and Tsai [19]. (b) A tetromino-based mosaic from Chang and Tsai [20].
22
Chapter 3
Line-based Cubism-like Image --- A New Type of Image and Its
Application to Data Hiding by Invisible Reversible Pixel
Re-coloring
3.1 Overview of Proposed Method
In this chapter, we describe how we create a type of art image like Cubism paintings automatically via the use of a computer, and we name this kind of art image
line-based Cubism-like image. By this type of art image, we try to keep a
characteristic of the Cubism art multiple viewpoints by the use of the line
feature. By rearranging lines in a given image, which are yielded by applying the Hough transform to the image, a line-based Cubism-like image is created, which includes a new three-dimensional shape of each identity in the given image. In Section 3.2, the proposed method for automatic creation of line-based Cubism-like images will be described in detail.In order to achieve the purpose of hiding information in this type of art image, we propose also a data hiding technique in this study. A given message is embedded into a line-based Cubism-like image during the stage of region coloring in the creation process of the image. We assign a new color to each image pixel by keeping unchanged the average of the color in the region which includes the pixel, and
23
re-coloring the pixel without causing a perceptible change. Furthermore, a technique is proposed to enhance the security of the hidden data by randomizing the processing order of the regions.
3.2 Proposed Line-based Cubism-like Image Creation Process
3.2.1 Idea of Proposed Creation Technique
Cubism artists transform a natural scene into geometric forms by breaking up, analyzing, and re-assembling objects in the scene. In addition, with the scene objects rearranged to intersect at random angles, each painting of Cubism seems to be composed of intersecting lines and fragmented shapes in an abstract style. The idea of the proposed art image creation method is inspired by this concept of Cubism, as mentioned previously.
In the creation process of a line-based Cubism-like image from a given image, at first we find the longer line segments in the source image by the Hough transform.
Then, we connect the line segments and extend them to reach the image boundaries.
Finally, we generate the desired art image via the operations of line segment merging and region re-coloring. This process accomplishes the goal of transforming the input image into an abstract form since the lines of the created Cubism-like image tend to constitute the skeleton of the objects in the source image as observed from according to our experimental results. The detailed algorithms of the above-mentioned processes are described in the following sections.
24
3.2.2 Proposed Art Image Creation Process
In this section, we present an algorithm which implements the idea of proposed Cubism-like image creation. Basically, in the process of line detection, we find edges of the source image by utilizing the Canny edge detection technique [21], and then perform the Hough transform on the edge detection result to obtain longer line segments in the source image. By extending and recombining these longer line segments, a desired Cubism-like image is created. The detailed algorithm is given as follows.
Algorithm 3.1: line-based Cubism-like image creation.
Input: a source image S, and two threshold values the minimum length L
min of a line segment, and the minimum distance Dmin between two lines.Output: a line-based Cubism-like image C.
Steps.
Stage 1 --- creating crossing-image lines.
Step 1. Perform Canny edge detection to find the edges E1, E2, …, En in source image S, resulting in a new image S′.
Step 2. Implement the following steps to find out longer line segments in S′.
2.1 Find the line segments L1, L2, …, Lm, in S′ by applying the Hough transform on S′, yielding a second new image S′′ of the line type.
2.2 Select those line segments in S′′ with their lengths larger than the threshold Lmin.
2.3 Compare every line pair Li and Lj with i j in S′′ in the following way:
if the distance Dij between Li and Lj is smaller than Dmin, then delete Li if the length of Li is smaller than that of Lj; or delete Lj, otherwise.
25
Step 3. Extend each of the remaining line segments in S′′ to the boundaries of S′′, and regard the source image S as being partitioned by the extended lines into regions.
Stage 2 --- re-coloring image regions.
Step 4. Create a binary image T with the same size as that of S with the initial pixel values all set to be 0.
Step 5. Fill the value of 1 into those pixels in T which correspond to those lying on each of the extended line segments in S′′.
Step 6. Implement following steps to recolor the regions in S.
6.1 Perform region growing in the binary image T in a raster-scan order, and segment out 0-valued regions, R1, R2, …, Rk, each of which is enclosed by a group of 1-valued line segments in S′′.
6.2 Compute the area Ai of each segmented region Ri in T and the average RGB color values (Cir, Cig, Cib) of the corresponding region Ri′ in S using Ai, and re-color each pixel in Ri′ of S by the color values (Cir, Cig,
C
ib), i = 1, 2, …, k.6.3 Re-color all lines in S corresponding to the 1-valued extended lines in
T by the white color.
Step 7. Take the final S as the desired line-based Cubism-like image C.
The above algorithm of line-based Cubism-like image creation, as illustrated in Figure 3.1, is composed of two stages. In Stage 1, we perform line detection to obtain the longer lines in a source image S. By Canny edge detection, we get a group of edge points. For the purpose of finding prominent line features in S, we use two thresholds to select the longer and sufficiently-separate lines from those line segments yielded by applying the Hough transform to the group of edge points. The first threshold Lmin is
26
used to filter out short line segments. The other threshold Dmin is used to filter out extended lines which are too close to other longer lines. The final step in this stage is to extend each of the remaining line segments to cross the image, with the two line
Figure 3.1 Process of crossing-image line creation.
In this study, after considering the mutual influence between the image size and the line length, we use one-tenth of the longer boundary length of the image as the initial value of Lmin and Dmin. A series of experiments about the effects of varying the values of Lmin and Dmin have been conducted, and an experimental result is shown in Figure 3.2. In these resulting images, we can find that if we take a smaller initial value
27
of Lmin, the number of extracted lines will increase. With more lines, the complexity of the created Cubism-like image also increases, giving an impression closer to the original image content. On the other hand, fewer lines make the Cubism-like image simpler and more abstract. The effect of changing the initial value of Dmin is similar to that of Lmin.
(a) (b)
(c) (d)
(e) (f)
Figure 3.2 An experimental result of varying the threshold values of Dmin and Lmin. (a) A source image with size 1024768. (b) A Cubism-like image created from (a) with initial Dmin = 102 and initial Lmin = 102. (c) A Cubism-like image created from (a) with Dmin = 20 and Lmin = 102. (d) A Cubism-like image created from (a) with Dmin = 102 and Lmin = 20. (e) A Cubism-like image created from (a) with Dmin = 200 and Lmin = 102. (f) A Cubism-like image created from (a) with Dmin = 102 and Lmin = 200.
28
In Stage 2, with the extended line segments, the source image S is regarded as being partitioned into regions. By region growing, we segment out these regions and calculate the area and the average RGB color of each of them. Finally, a line-based Cubism-like image C is created by re-coloring these regions with the average color and all the lines with the white color.
3.2.3 Experimental Results
According to the above discussions, we see that different selections of the two threshold values Lmin and Dmin will result in totally different effects. However, it is difficult to decide which result is better than the others because the decision is obviously dependent on the different feelings of people for art. Therefore, in this study we just offer a series of results yielded by the use of different sets of thresholds for the user to choose. Specifically, we use the normalized initial thresholds of 1/10 of the length of the longer image boundary as the center, and vary each threshold to be twice and half of its initial value, in addition to the initial one. As a result, each threshold has three choices, resulting in nine choices of the two threshold values.
Then, we generate nine art images, each corresponding to one of the nine threshold combinations, for the user to choose his/her favorite one among them.
Besides, we also provide the option of choosing normalized thresholds for users, and then we produce the nine sets of threshold combinations as described above based on the choice of the user. Some Cubism-like images created by the above-proposed algorithms with nine results yielded by the use of different threshold combinations for each input source image are given in Figures 3.3 through 3.5. For simplification, we use the expression (Dmin, Lmin) to show a combination of the two thresholds in the captions of the figures. As seen in the images, we can see an abstract style of Cubism