Report On HIDING DATA IN IMAGES BY SIMPLE LSB SUBSTITUTION

In this paper, a data-hiding scheme by simple LSB substitution is proposed. By applying an optimal pixel adjustment process to the stego-image obtained by the simple LSB substitution method, the image quality of the stego-image can be greatly improved with low extra computational complexity. The worst case mean-square-error between the stego-image and the cover-image is derived. Experimental results show that the stego-image is visually indistinguishable from the original cover-image. The obtained results also show a significant improvement with respect to a previous work.

Keywords: Data hiding; LSB substitution

1.      Introduction

Data hiding is a method of hiding secret messages into a cover-media such that an unintended observer will not be aware of the existence of the hidden messages. In this paper, 8-bit grayscale images are selected as the cover media. These images are called cover-images. Cover-images with the secret messages embedded in them are called Stego-images. For data hiding methods, the image quality refers to the quality of the stego-images.

In the literature, many techniques about data hiding have been proposed . One of the common techniques is based on manipulating the least significant bit (LSB) planes by directly replacing the LSBs of the cover-image with the message bits. LSB methods typically achieve high capacity.

Wang et al. proposed to embed secret messages in the moderately significant bit of the cover-image. A genetic algorithm is developed to find an optimal substitution matrix for the embedding of the secret messages. They also proposed to use a local pixel adjustment process (LPAP) to improve the image quality of the stego-image. Unfortunately, since the local pixel adjustment process only considers the last three least significant bits and the fourth bit but not on all bits, the local pixel adjustment process is obviously not optimal. The weakness of the local pixel adjustment process is pointed out in Ref. . As the local pixel adjustment process modifies the LSBs, the technique cannot be applied to data hiding schemes based on simple LSB substitution.

Recently, Wang et al.  further proposed a data-hiding scheme by optimal LSB substitution and genetic algorithm. Using the proposed algorithm, the worst mean-square-error (WMSE) between the cover-image and the stego-image is shown to be 1/ 2 of that obtained by the simple LSB substitution method.

                In this paper, a data-hiding scheme by simple LSB substitution with an optimal pixel adjustment process (OPAP) is proposed. The basic concept of the OPAP is based on the technique proposed in Ref . The operations of the OPAP is generalized. The WMSE between the cover-image and the stego-image is derived. It is shown that the WMSE obtained by the OPAP could be less than 1/2 of that obtained by the simple LSB substitution method. Experimental results demonstrate that enhanced image quality can be obtained with low extra computational complexity. The results obtained show better performance than the optimal substitution method described in Ref. 

                The rest of the paper is organized as follows. Section 2 briefly describes the simple LSB substitution. In Section 3, the optimal pixel adjustment process is described and the performance is analyzed. Experimental results are given in Section 4. Finally, Section 5 concludes this paper.

Moreover, the optimal pixel adjustment process only requires a checking of the embedding error between the original cover-image and the stego-image obtained by the simple LSB substitution method to form the final stego-image. The extra computational cost is very small compared with Wang’s method , which requires huge computation for the genetic algorithm to find an optimal substitution matrix.

4. Experimental results

This section presents experimental results obtained for two cover-image sets. The first set of cover-images consists of four standard grayscale images, 'Lena', 'Baboon', 'Jet' and 'Scene', each of 512 ×512 pixels, as depicted in fig. 1.

                The second set consists of 1000 randomly generated grayscale images. There are two set of secret messages.  The first set of secret message consists of 1000 randomly generated message of 512 × 512 × k bits, where k refers to the number of LSBs in the cover image pixels that are used to hold the secret data bits. For example, suppose that the last two LSBs of the cover image pixels are used to hold the secret data, then the secret data is of size 512 × 512 × 2 = 524 288 bits. The second set consists of the reduced-sized images of the grayscale image 'Tiff' as shown in fig. 2.

The reduced-sized images are of size 512 × 256 pixels (for 4-bit insertion), 384 × 256 pixels (for 3-bit insertion), 256 × 256 pixels (for 2-bit insertion) and 256 × 128 pixels (for 1-bit insertion), respectively. The results of embedding the first set of secret messages into the first set of cover-images are listed in Table 2. Referring to Table 2, the column labeled OPAP is our proposed Table 2, method with the optimal pixel adjustment process; the column labeled LSB is the simple LSB substitution method; and the column labeled OLSB in the optimal LSB substitution method proposed in Ref. . For the OPAP and LSB methods, the obtained PSNR values are the average values of embedding the 1000 sets random messages into the cover-images. For the OLSB method, for k =1; 2, the obtained PSNR values are the average values of embedding the 1000 sets random messages into the cover-images, for k = 3, the obtained PSNR values are the average values of embedding the 10 out of 1000 sets random messages into the cover-images while for k = 4, no experiments are conducted due to the large number of searching space for the optimal substitution matrix. The results reveal that our proposed method has much better performance than the LSB and OLSB methods for k =2-4.

The results of embedding the reduced-sized image of fig. 2 into the first set of cover-images are listed in Table 3. The results also reveal that our proposed method has much better performance than the LSB and OLSB methods for k =2-4.

Table 2.

The results of embedding the random messages into the first set of cover-images               

Cover image	k	OPAP	LSB	OLSB
Lena	1	51.1410	51.1410	51.1483
	2	46.3699	44.1519	44.1651
	3	40.7271	37.9234	37.9467
	4	34.8062	31.7808	-
Baboon	1	51.1414	51.1414	51.1477
	2	46.3691	44.1579	44.1619
	3	40.7253	37.9226	37.9480
	4	34.8021	31.8588	-
Jet1	1	51.1405	51.1405	51.1478
	2	46.37000	44.1149	44.1276
	3	40.7273	37.9557	37.9978
	4	34.8065	31.8487	-
Scene1	1	51.1410	51.1410	51.1480
	2	46.3702	44.1497	44.1628
	3	40.7270	37.8914	37.9849
	4	34.806	31.8467	-

Table 3

The results of embedding the reduced-sized image of fig. 2 into the first set of cover-images

Cover image	k	Case 1(%)	Case 2(%)	Case 3(%)	Case 4(%)	Case 5
Lena	2	9.52	0	86.55	3.93	0
	3	14.15	0	80.86	4.99	0
	4	21.30	0	73.27	5.43	0
Baboon	2	9.53	0.01	86.51	3.95	0
	3	14.03	0.02	80.90	5.05	0
	4	20.78	0.05	73.85	5.32	0
Jet	2	9.67	0	86.32	4.01	0
	3	13.91	0	81.20	4.89	0
	4	20.31	0	74.22	5.47	0
Scene	2	9.58	0	86.53	3.89	0
	3	14.17	0.01	80.78	5.04	0
	4	21.01	0.01	73.74	5.24	0

Table 4

The percentage of cover image pixels associated with the five cases (Eq.12) when the reduced-sized images of Fig.2 are embedded into the cover images.

Cover image	k	Case 1(%)	Case 2(%)	Case 3(%)	Case 4(%)	Case 5
Lena	2	9.52	0	86.55	3.93	0
	3	14.15	0	80.86	4.99	0
	4	21.30	0	73.27	5.43	0
Baboon	2	9.53	0.01	86.51	3.95	0
	3	14.03	0.02	80.90	5.05	0
	4	20.78	0.05	73.85	5.32	0
Jet	2	9.67	0	86.32	4.01	0
	3	13.91	0	81.20	4.89	0
	4	20.31	0	74.22	5.47	0
Scene	2	9.58	0	86.53	3.89	0
	3	14.17	0.01	80.78	5.04	0
	4	21.01	0.01	73.74	5.24	0

               

For illustrative purpose, fig. 3 shows a pair of stego-images obtained by embedding the reduced-sized image 'Tiff' of size 512 × 256 pixels into the cover-image 'Lena' of size 512 × 512 pixels using the simple LSB method and the proposed OPAP method. From fig. 3(a) (stego-image obtained by the simple LSB-substitution method), one can see some false contours appearing on the shoulder of 'Lena'. The unwanted artifacts may arise suspicion and defeat the purpose of steganography. However, there is no such artifacts appearing on the stego-image (fig. 3(b)) obtained by the proposed method. The visual quality of stego-images obtained by the proposed method is much better than that of obtained by the simple LSB-substitution method.

To further evaluate the performance of the proposed method, the reduced-sized image of fig. 2 is embedded into 1000 sets randomly generated cover-images and the obtained average PSNR values are listed in Table 5.

Table 5

The results of embedding the reduced-sized image of fig. 2 into the second set of cover-images.

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Cover image      k            OPAP                 LSB             

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Random             1            51.1410               51.1410    

                          2            46.3215               44.0217            

                          3           40.6023                37.8621     

                          4           34.4868                 31.337

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The results show that similar PSNR values can be obtained for different type of cover-images.

5. Conclusion:

In this paper, a data hiding method by simple LSB substitution with an optimal pixel adjustment process is proposed.  The image quality of the stego-image can be greatly improved with low extra computational complexity. Extensive experiments show the effectiveness of the proposed method.  The results obtained also show significant improvement than the method proposed in Ref.  with respect to image quality and computational efficiency.