A Novel Scheme of Data Hiding in Binary Images Hema Ajetrao

Pad. Dr.D.Y.P.I.E.T,Pimpri,Pune Dr.P.J.Kulkarni

Walchand College of Engineering,sangli Navanath Gaikwad

IBM India Pvt Ltd.Pune

Abstract

This paper proposes a new method to embed data in compressed binary images (JPEG), including scanned text, figures, and signatures. The method manipulates “flippable” pixels to embed data without distorting image. Shuffling is applied before embedding to provide more security. The hidden data can be extracted without using the original image. The proposed method uses look up table which is to be used for extraction of data at other end is made more compact, secure. After applying proposed method the image will be distorted no drought but human eye will not recognize the change in the image. The proposed data embedding method can be used to detect unauthorized use of a digitized signature, and annotate or authenticate binary documents. Keywords:--Data Hiding, Binary Images, Annotation and Authentication 1. Introduction

Digital watermarking and data hiding techniques have been proposed for a variety of digital media applications, including ownership protection, copy control, annotation, and authentication. Most prior works on image data hiding are for color and grayscale images in which the pixels take on a wide range of values [2]. For most pixels, changing the pixel values by a small amount may not be noticeable under normal viewing conditions. In particular, flipping white or black pixels that are not on the boundary is likely to introduce visible artifacts in binary images. Hiding data in binary image, though difficult, is getting higher demands from our everyday life. An increasingly large number of digital binary images have been used in business. Handwritten signatures captured by electronic signing pads are digitally stored and used as records for credit card payment by many department stores and for parcel delivery by major courier services such as the United Parcel Service (UPS) [7].This method is used for such scanned images.

1.1 Least Significant Bit Insertion

Usually 24-bit or 8-bit files are used to store digital images. The former one provides

more space for information hiding; however, it can be quite large. The colored representations of the pixels are derived from three primary colors: red, green and blue. 24-bit images use 3 bytes for each pixel, where each primary color is represented by 1 byte. Using 24-bit images each pixel can represent 16,777,216 color values. We can use the lower two bits of these color channels to hide data, then the maximum color change in a pixel could be of 64-color values, but this causes so little change that is undetectable for the human vision system. This simple method is known as Least Significant Bit insertion

Using this method it is possible to embed a significant amount of information with no visible degradation of the cover image. Several versions of LSB insertion exist. It is possible to use a random number generator initialized with a stego-key and its output is combined with the input data, and this is embedded to a cover image. For example in the presence of an active warden it is not enough to embed a message in a known place (or in a

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known sequence of bits) because the warden is able to modify these bits, even if he can’t decide whether there is a secret message or not, or he can’t read it because it is encrypted. The usage of a stego-key is important, because the security of a protection system should not be based on the secrecy of the algorithm itself, instead of the choice of a secret key. The LSB inserting usually operates on bitmap images [8].

Flippable Pixel Technique is a new approach that can hide a moderate amount of data in general binary images, including scanned text, figures, and signatures. The hidden data can be extracted without using the original unmarked image. The approach can be used to verify whether a binary documents that has been tampered and to hide annotation labels or to hide other side information. There is no need to have cover file for extraction at receiver. After embedding data the size of the cover file remain same as the stego file. The block diagram as shown in figure 1 shows that the steps to be followed for hiding data in cover image file and extracting data from stego file.

Embedding e Extraction

Figure.1 Block Diagram of Embedding and Extraction Mechanism for Binary Image

2. Proposed Method

2.1 Details of Determining Flippability Scores

The smoothness of the neighborhood around pixel (i, j) is measured by the total number

of horizontal, vertical, diagonal, and anti-diagonal transitions in the 3x3 block respectively [7], using a differential operator along the corresponding directions as shown in figure 2. For simplicity, we shall illustrate the evaluation method for binary image. The scores will be used to determine which pixel will be flipped with high priority during the embedding process.

Step-1 Compute smoothness and connectivity of 3 × 3 pattern [7].

Horizontal 1 0 Nh(I, J)= ∑ ∑I({pi+k,j+l ≠ pi+k,j+l+1}),

Input Image Shuffle

Compute flippabilty score Embed

Secret data

Inverse shuffle

Stego Image

Stego Image Shuffle Extract Secret

data

Look up table

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Vertical 1 0 Nv(I, J)= ∑ ∑I({pi+l,j+k ≠ pi+l+1,j+k}), k=-1 l=-1 Diagonal Nd1(I,J)= ∑ I({pi+k,j+l ≠ pi+k+1,j+l+1}), K, 1∈{-1,0} Anti –Diagonal Nd2(I,J)= ∑ I({pi+k,j+l ≠ pi+k-1,j+l+1}) K∈{0,1},l∈{-1,0} Where I(·) is the indicator function taking value from {0, 1}, and pi,j denotes the pixel

value of the ith row and jth column of the whole image. The connectivity is measured by the number of the black and white clusters. For this

purpose, the connectivity criterion between two pixels needs to be prescribed. A commonly used criterion, illustrated in Fig. 2, considers the pixels that touch each others by 90-degree (i.e., (i, j ± 1) or (i ± 1, j)) or by 45-degree (i.e., (i + 1, j ± 1) or (i − 1, j ± 1)) and that have the same pixel value as connected 2 Depending on the specific constraints of visual artifacts, 45-degree touching may not always be considered as connected. Using the criterion, we can build a graph for black (or white) pixels. In the graph, each vertex represents a black (or white) pixel, and there is an edge between two vertices if and only if the two corresponding pixels are connected [7]. An example is shown in Fig. 4 with five black pixels forming two clusters and four white pixels forming one cluster.

Fig.ure 2: Illustration of transitions in four directions, namely, horizontal, vertical, diagonal, and anti-diagonal. The number of transitions is used to measure the smoothness of the 3 × 3 neighborhood.

Figure. 3: The pixel that touch each other by 900 (i.e.. (I,j+1), or (I+1,j) ) and that have the same pixel value as connected. The lightly shaded pixels in this figure are considered as touching the central pixel by 900.

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3x3 patterns Connectivity graphs for black pixels

With two clusters and for white pixels with one cluster respectively

Figure. 4: Graph representation of the connectivity for black and white pixels, showing here is an example of 3x3 patterns with five black pixels forming two clusters and with four white pixels forming one cluster.

Step-2 Compute flippability score [7].

The smoothness and connectivity measures are passed into a decision module to come up with a flippability score. Main considerations when designing this module are whether the original pattern is a very smooth pattern ,whether flipping will increase non-smoothness by a large amount & whether flipping will cause the change of connectivity.

Listed here are the rules used by our decision module.

1) The zero score is assigned to uniformly white or black regions 2) If the no of transitions along the horizontal and vertical direction is zero, Assign

zero as a final score for the current block, otherwise assign horizontal, vertical transitions as score to the current block.

3) If the no of transitions along diagonal and anti-diagonal direction is zero, assign zero as a final score for the current block, otherwise assign diagonal, anti-diagonal transitions as score to the current block.

4) Calculate the no of clusters in the block and add to the final score [if flipping pixel increases no of clusters then skip the block].

Finally we have taken those blocks whose connectivity is greater than equal to two and

smoothness is greater than 3 .If we lessees the above criteria the image gets distorted.

Figure. 5: Flippability score calculated for 3x3 patterns

2.2. Shuffling Shuffling provides level of security to our data and also provides uneven embedding

capacity throughout the image [6][7].

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2.3. Embedding The entire process of embedding and extraction is illustrated in Fig. 1. Directly encoding

the hidden information in flippable pixels (e.g., set to 1 if to embed 0 and 0 to embed 1) may not allow the extraction of embedded data without original image. The reason is that the embedding process may change a flippable pixel in the original image to a pixel that may no longer be considered flippable. As a simple example, suppose only black pixels that are immediately adjacent to white pixels are considered as ‘‘flippable’’. One such flippable pixel, marked by thick boundary in Fig. 6(a), is changed to white to carry a “1”, as shown in Fig. 6(b). It can be seen that after embedding, this pixel is no longer considered flippable if applying the same rule. This simple example shows the difficulty for the detector to correctly identify which pixel carries hidden information without using the original image. Instead of encoding the hidden information directly in flippable pixels, we embed data only on those blocks whose smoothness is greater than 3 and whose connectivity is greater than equal to two. Again to embed a “0” in a block, some pixels are changed so that the total number of white pixels in that block is an odd number. Similarly, to embed a “1”, the number of white pixels is enforced to an even number. In this paper the enforcing of odd or even number of white pixels in a block is used. If flipping the current pixel increases the no of cluster of the block then skip the block. Lookup table contains the block that has hidden information.

Figure.6 Two examples of 3x3 neighborhood for which flipping the center pixel to white in (a) is less noticeable than (b)

2.4. Inverse Shuffling

It is exactly opposite procedure of that we have performed in the shuffling. This stage leads to convert into stego image.

2.5. Extraction

Figure 1 gives brief idea about extraction process. Stego image is given as input, we are

shuffling each block of stego image as per shuffling mechanism of the embedding process.. Lookup table provides blocks of image, which contains secret bits. Then we are applying odd-even technique for each block, which leads to extraction of secret data.

2.6. Details of creating look up table

This look up table will send on the transmission line to be used for extraction of data

from stego image. The look up table will consist the block numbers that is supposed to be having enough roughness to embed data. For example block number 5, 10, 15 is having data embedded. Look up table entry will have 5, 10, 15 block numbers. In our method we are using reference string i.e. 1 to 4 will have 0 value in look up table and 5th position of reference string will be 1. Like that 10th position of reference string will be 1.So the reference string will become 0001000001000010.The reference strings bytes taken to convert it to integer value 10 42 .These values are taken again to represent character. The

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look up table will now consist ‘o ‘.This is the way we reduce the size of look up table and provides more security.

3. Experimental Results

In this section, experimental results are illustrated to demonstrate the validity of our

proposed data hiding and extraction mechanism by using Various binary images and Signature Binary images, such as Monkey. jpg & lenna.jpg as well as two signature images The experimental results reveal that our proposed scheme exhibit very good performance. Experiments were conducted by hiding the data as shown in Figure 7 on various testing images. The performance generated by our proposed method Is tabulated in Table 2.From the experimental results we find that the data hiding capacity of our method is moderate as compared to the LSB & MSB data hiding technique.. As to the visual quality of the embedded images evaluated by using SNR, our method is negligible and small .So we said the image gets distorted but human eye cannot recognize that distortion. The hiding capacity of the image can be increased if the image consists of more rough blocks[i.e. the no of transitions are more & more connectivity groups].

The proposed method can also be utilized for signature verification and perform tampering detection if the embedded images are tampered. The experimental result of tampering detection is illustrated We can detect the occurrence of tampering because the hidden data extracted from the tampered image is different from the hidden data.

Table 1.The Hiding Capacity of Various Images

Sr. No. Image Name Image Size DHC Percentage 1 Monkey .jpg 512*512 14686 5.6 % 2 Lenna .jpg 512*512 4150 1.58 % 3 Signature1.jpg 562*195 1122 1.02 % 4 Signature2.jpg 576*254 1850 1.26 %

Table 2. The SNR Difference after Hiding Data

Sr.

No, Image Name Image

Size SNR Of

Cover SNR

Of Stego Difference

1 Monkey .jpg 512*512 1.044 1.015 0.029 2 Lenna .jpg 512*512 1.101 1.094 0.007 3 Signature1.jpg 562*195 0.981 0.632 0.349 4 Signature2.jpg 576*254 1.440 1.071 0.369

Signature image 2 after embedding 1850 bits

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Monkey.jpg After embedding 14000 bits

Lenna.jpg after embedding 4150 bi

Signature Image 1 after embedding 1122 bits Figure 7:-- the Various Binary images after embedding maximum data.

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. We describe here a flippable pixel technique to hide data in compressed binary image for authentication & annotation. Mainly we worked on to annote signature images & send it on transmission line. The method manipulates flippable pixels to enforce a specific block based relationship to embed a significant amount of data without causing noticeable artifacts. Shuffling is applied for more security. The importance of this particular is that it doesn’t require the cover file should be sent with the stego file for Steganalysis. Work on the system This method can hide fewer amounts of data into binary images. To hide large amount of data in images, technique for color images need to be developed.

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[4] M. Wu and B. Liu, “Watermarking for image authentication,” in Proc. IEEE Int. Conf. Image

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[5] M. Wu and B. Liu, “Data hiding in image and video: Part-I—Fundamental issues and solutions,” IEEE Trans. Image Processing, vol. 12, pp. 685–695, June 2003.

[6] M. Wu. (2001) Multimedia Data Hiding, Ph.D. dissertation. Princeton [17] M. Wu and B. Liu, “Digital watermarking using shuffling,” in Proc IEEE ICIP’99, vol. 1, Kobe, Japan, 1999, pp. 291–295.

[7] Min Wu, Bede Liu, ”Data Hiding in Binary Image for Authentication and Annotation” IEEE

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[8] Robert Krenn “Stenography & Steganalysis”

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