Data Hiding of Binary Images Using Pair-wise Logical Computation ...

Data Hiding of Binary Images Using Pair-wise Logical Computation Mechanism Chang-Lung Tsai

Kuo-Chin Fan Char-Dir Chung Thomas Chiang Chuang$  Department of Electrical Engineering

Institute of Computer Science and Information Engineering National Central University, Taiwan, R.O.C. $ Van Nung Institute of Technology [email protected]; [email protected]; [email protected] Abstract

proposed the concept of non-uniform blocks and employed 2-D shifting for hiding data in binary document images to achieve the goals of tamper proofing and authentication. As for binary images, those marks might be hidden on the boundary pixels of partitioned blocks of the cover image. In this paper, we propose a novel binary data hiding mechanism that can achieve the goals of reversible and lossless simultaneously, i.e., it will not destroy or damage the recovered host image after extracting the hidden data. Moreover, we adopt the rationale of HVS [4] [5] to evaluate the performance of our proposed method. Experimental results demonstrate that the visual quality and the data hiding capacity of our proposed approach are both superior to those of traditional approaches.

Due to the emerging of digital library and fast development of multimedia, more and more people use data hiding techniques to hide annotations or side information in images. In this paper, we propose a novel data hiding mechanism by hiding data based on pair-wise logical computation. The proposed mechanism can achieve the benefits of reversible and lossless reconstruction of hidden data and host image without utilizing any information from the original host image. It will not degrade the visual quality of the recovered host image after extracting the hidden data. Moreover, satisfactory data hiding capacity can be obtained simultaneously. The proposed data hiding mechanism is suitable to be applied to the data hiding of images, scanned texts, figures, and signatures, especially for side information and annotation data embedding.

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The rationale of our proposed data hiding approach is implemented by the following procedure: Step 1: Transfer the hidden data into M-sequence hidden binary bit stream. Step 2: Insert a reference bit “1” in front of each bit in the M-sequence hidden binary bit stream to form a new binary sequence with 2M bits. Group each two bits in the new binary sequence to form a pair. The new binary sequence will thus possess M pairs. Step 3: Select suitable places in the host image pairwisely to embed the hidden data row by row and column by column. Step 4: Perform pair-wise logical computation as described later to determine the data to be hidden. The hidden data can be extracted by performing the above steps reversely.

1. Introduction Hundred years ago, human beings had used secret writing and steganography to hide and protect information [1]. Both secret writing and steganography use an unsuspicious cover media to protect and carry important message. Currently, the hiding of annotation, side information or confidential data inside an image covered by its unsuspicious outlook arise certain interest in some applications, such as digital library. However, existing data hiding techniques can not achieve the goals completely, especially for lossless recovery of original host images. It is admitted that the goals are even more difficult to be achieved for binary images. Most of the data hiding techniques for binary document images are implemented by line shifting, word shifting or feature shifting coding [2]. In [3], Lu 1

0-7803-8603-5/04/$20.00 ©2004 IEEE.

The Proposed Approach

The locations with pixel value transition, i.e., boundaries, are also suitable positions for hiding data. The reason is that it will not degrade the visual quality of the embedded image by hiding data near the boundaries because it is less sensitivity to the perception of human beings. In our work, the pixel pair (bit pair) next to the transition location is also chosen to perform the data hiding process. The data hiding process is performed from left to right and then top to bottom in the host image, i.e., horizontally, with the hidden information. We can also adopt another alternative by performing the data hiding process vertically, i.e., from top to bottom and then from left to right.

2.1 PWLC data hiding The first data hiding scheme that we proposed is called pair-wise logical computation (PWLC) data hiding which is implemented based on the rationale of pair-wise exclusive-or logical computation in determining the data to be hidden. As we know from the basic manipulation of exclusive-or operation, we can not uniquely determine the two possible exclusive-or input combinations without knowing one of the two input variables. In step 2 of the proposed data hiding mechanism, a reference bit with value “1” is inserted in front of each individual bit of the hidden information to form a hidden bit pair. The proposed mechanism is designed by selecting the two adjacent pixels with the same value from the host image as the suitable bit pair. Only in this way, the hidden information can be correctly recovered by performing the reverse exclusive-or operations between the hidden bit pair and the hidden data. The reasons why we do not process every host image bit pair are twofolds: (1) the embedded image will be damaged severely, i.e., the visual quality of the embedded image will be degraded, by processing every bit pair in the host image, (2) Since the position of the bit pair in the host image is the place of data to be hidden, the hidden information can not be completely recovered by processing every bit pair in the host image. The performing of exclusive-or operation between the bit pair in the host image and the hidden bit pair of hidden information can be summarized by the following equations: (1) RBʃH1=Emed1 HBʃH2=Emed2 (2) where RB is the inserted reference bit value which is always a “1”, HB is the data bit value in the hidden information, and H1 and H2 is a bit pair in the host image with the same value. All possible manipulations in the two equations can be listed by a data hiding look-up table as tabulated in Table 1. Table 1: Data hiding look-up table Host image Bit pair 1st 2nd pixel pixel 0 0 1 1

2.2 Boundary-based PWLC data hiding Now, let us slightly modify the data hiding process which is unlike the traditional block-based approach by selecting the flippable pixel [6] to perform the embedding process. The rationale of the modified data hiding scheme called boundary-based PWLC data hiding is the same as that of PWLC data hiding. The only difference is that the choosing of hidden places which are located on the boundaries of host images. In the boundary-based PWLC data hiding mechanism, boundary detection is first executed on the host image to find the boundary. Then, trace the boundary counterclockwisely from left to right and then top to bottom (scanning sequence) in the host image. Next, remove those branching boundaries and discontinuous boundaries from the result generated in the boundary tracing process. Last, select the boundary point whose value equals the value of its adjacent rows or columns in the host image to form a continuous 1D boundary pixel string. The PWLC data hiding process is then executed along the 1D boundary pixel string. Since the pixel values in the boundary pixel string are all the same, only consecutive “11” bit pairs will appear in the 1D boundary pixel string. Experimental results demonstrate that the visual quality of the embedded images generated by the boundary-based PWLC data hiding scheme is superior to that generated by PWLC data hiding scheme. In order to achieve completely reversible data hiding and extraction, the selection of hidden place have to be carefully considered. Otherwise, it is difficult to distinguish whether the value change of a pixel is incurred due to the data hiding process or the boundary shift phenomenon, i.e., the embedded image will form discontinuous boundary and will be discarded in the data extraction process. In boundary-based PWLC data hiding, we have to select the boundary point whose value equals the value of its adjacent rows and columns in the host image.

Embedded Data Reference bit ‘1’ 1 0

Hiding bit ‘0’ 0 1

Reference bit ‘1’ 1 0

Hiding bit ‘1’ 1 0

There are two possible combinations of hidden bit pair (10 and 11) and two possible combinations of host image bit pair (00 and 11). Hence, four possible combinations are derived from the corresponding bit pair and hidden bit pair exclusive-or operation. To completely recover the hidden information, we only process the host image bit pairs that possess three consecutive “00” or “11” bit pairs and the middle bit pair is chosen for performing the data hiding process. 2

experimental results reveal that our proposed scheme indeed exhibit very good performance.

2.3 Hidden Data Extraction In general, the hidden data can be extracted by performing the blind or non-blind detection process. The algorithm proposed in this paper belongs to the category of blind detection. In our work, the embedded image is directly utilized to extract the hidden information without the need of estimated or predicted original host image. Hence, the hidden information can be extracted without damaging the visual quality of reconstructed host image. The proposed data hiding and data extraction process are reversible. Since the value of reference bit RB is known as “1” and H1 and H2 possess the same value, we can derive the values of Emed1 and Emed2 from the following equations. RB † H 1 Emed 1 Ÿ 1 † H 1 Emed1 (3) HB † H 2 Emed 2 Ÿ HB † H 1 Emed 2 (4) From equation (4), we know that if H1 equals 0, then HB will be equal to Emed2. If H1 equals 1, HB will then be the complement of Emed2. The results are summarized as tabulated in Table 2. Table 2: Hidden data extraction look-up table. Received pixel pair Emed1 0 0 1 1

Emed2 0 1 0 1

3.1 Experimental Results Fig 1 shows the result of data embedding for binary images generated by PWLC data hiding scheme and boundary-based PWLC data hiding scheme. Experimental results demonstrate that the data hiding capacity and the visual quality of embedded image are both satisfactory. The PWLC and boundary-based PWLC data hiding schemes are also suitable for document images data hiding. In performing the document image data hiding, the embedded data is hidden directly in the boundary of every character without forming 1D boundary string because the size of characters is too small. Since there are more boundaries in document images, i.e., the entropy or information content is richer, the visual quality of embedded document images will be better than that of binary images. The comparison between our approach and Tseng’s method is illustrated in Fig 2. It reveals that our proposed scheme achieve better performance than that of Tseng [7] in both visual quality and data hiding capacity. Besides, we do not need to manually delimitate a block to embed header information or relating embedding parameters as required in Tseng’s algorithm. We also theoretically compare the data hiding capacity between our proposed mechanism and Wu’s algorithm [6]. It demonstrates that the data hiding capacity of our proposed scheme is higher than Wu’s algorithm. Moreover, almost all embedded document images can still be correctly recognized by commercial optical character recognition (OCR) system.

Hidden Data Extraction RB = “1” HB H 1= H2 1 1 1 0 0 0 0 1

To extract the hidden data embedded by boundary-based PWLC data hiding mechanism, both embedded image and the boundary image of the embedded image have to be utilized in the data extraction process. Since the hidden places on the boundary points have been carefully selected in the data hiding process, the reversibility is guaranteed in the data extraction process. First, the closed-loop boundary after data hiding is superimposed on the host image to generate the embedded image. Next, obtain the boundary image of the embedded image. Then, remove the branching boundaries and discontinuous boundaries in the embedded boundary image. Last, extract the hidden data according to the hidden data extraction look-up table as tabulated in Table 2.

3.2 Discussions Most of the data hiding techniques focus on completely recovering the hidden information without caring the distortion of reconstructed host image. Recently, the hiding of side information or annotation relating to the host images (such as art painting) becomes more and more important, especially for the application in digital library. In such application, the hidden data has to be extracted correctly without damaging the visual quality of reconstructed host image. By applying our proposed data hiding scheme, the hidden information and the host image can both be extracted and reconstructed without distortion. Generally speaking, the transmission of information that has been hidden into a covered host image is much safer than that by transmitting the information itself directly. However, binary image data hiding is more difficult to reach the goal of sufficient safety

3. Experimental Results In this section, experimental results are illustrated to demonstrate the feasibility and validity of our proposed data hiding and extraction mechanism. Various binary images and document images, such as Mickey Mouse, Minnie Mouse, and English text are chosen as the tested image in the experiment. The

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than gray-scale or color images from information theory because it is easier for human beings to perceive the suspicious hidden data in binary images by human visualization, i.e., the outlook of the embedded binary images is more noticeable than that of gray-scale images. Especially, those isolated pixels in the binary images are more oblivious due to the lacking of information content. As stated previously, the characteristic of our proposed data hiding mechanism is that it is reversible without utilizing the original host image in the data hiding and extraction processes. Under normal situation, i.e., without tampering, the hidden information can be correctly extracted and the host image can be completely recovered without distortion. If the embedded image is tampered during the transmission, the recovered host image will be different from that without tampering due to the information difference. Hence, we can easily find out whether the embedded image is tampered or not by comparing the recovered image processed by our data extracting process.

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[5]

[6]

[7]

Binary Document Images for Authentication”, IEEE, 2003 Ju Y., Minsoo J., Yong H., and Ro M., “Spatial Frequency Band Division in Human Visual System Based-Watermarking”, IWDW, Korea, Oct. 2003, vol. 2613, pp. 224-234 Mohammad A. and Mohan S. K., “Lossless Watermarking Considering the Human Visual System”, IWDW, Korea, Oct. 2003, vol. 2939, pp. 581-592 Wu M., Tang E., and Liu B., “Data Hiding in Digital Binary Image”, IEEE International Conference on Multimedia and Exposition, New York, vol. 1, 2000 Tseng Y. C. and Pan H. K., “Secure and Invisible Data Hiding in 2-color Images”, in Proc. IEEE INFOCOM, 2001, pp. 887-896

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4. Conclusions The hiding of side information or annotation into host images gradually attracts the attention of researchers due to the emerging of digital library. In addition to the hiding of information, the host image has to be losslessly recovered after extracting the hidden information. In this paper, we propose a novel pair-wise data hiding mechanism to achieve the aforementioned goals. The characteristic of our proposed data hiding mechanism is that it possesses the advantages of reversibility, lossless, and low visual quality degradation. Our proposed data hiding mechanism is reversible and lossless without harming the visual quality of reconstructed host images in data extraction process and it is very easy to implement by incorporating simple logical operations and boundary tracing. Currently, the proposed data hiding mechanism can only be applied to binary images, such as ordinary binary images and document images. In the future, we plan to extend the proposed PWLC concept to the data hiding of gray-scale and color images.

Figure 1: (a)Host image “Minnie” with size 96x96, (b)embedded image “Minnie” with 100 bytes hidden data using PWLC data hiding,(c)embedded image “Minnie” with 100 bytes hidden data using boundary-based PWLC data hiding,(d)host mage “Mickey” with size 96x96,(e)embedded image “Mickey” with 100 bytes hidden data using PWLC data hiding,(f)embedded image “Mickey” with 100 bytes hidden data using boundary-based PWLC data hiding.

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References [1] Steinder M., Iren S., and Amer P. D., “Progressively Authenticated Image Transmission,” in Proc. Military Communications Conference, vol.1, 1999, pp.641-645. [2] Low S. H., Maxemchuk N. F., Brassil J.T., and O’Gorman L., “Document Marking an Identification Using Both Line and Word Shifting”, Proc. INFOCOM, Boston, MA, Apr. 1995, pp. 853-860. [3] Lu H., Kot A. C., and Cheng J., “Secure Data Hiding in

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Figure 2: (a) Embedded image “Mickey” with 816 hidden bits by Tseng’s data hiding,(b) embedded image “Mickey” with 816 hidden bits by PWLC data hiding,(c) embedded image “Minnie” with 855 hidden bits by Tseng’s data hiding,(d) embedded image “Minnie” with 855 hidden bits by PWLC data hiding.

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