Literature Survey on Performance of Reversible Data Hiding Algorithm
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1 Literature Survey on Performance of Reversible Data Hiding Algorithm Sona Ignacious PG Scholar Department of ECE, PET Engineering College, India Abstract-DATA hiding technique aims to embed some secret information into a carrier signal by altering the insignificant components for copyright protection or covert communication. In general cases, the data-hiding operation will result in distortion in the host signal. However, such distortion, no matter how small it is, is unacceptable to some applications, e.g., military or medical images. For this purpose many data hiding algorithms have been proposed. This literature survey discusses all the existing data hiding algorithms and their performance. Keyword: Secret Information, Data Hiding, Distortion, Hiding Algorithm 1. INTRODUCTION Reversible data hiding was first proposed for authentication. Early reversible algorithms often have small embedding capacity and poor image quality. In most cases of data hiding, the cover media will experience some distortion due to data hiding and cannot be extracted back to the [original cover media, because some permanent distortion has occurred to the cover media even after the hidden data have been retrieved out. In some applications, such as medical diagnosis and law enforcement, it is difficult to reverse the marked media back to the original cover media after the hidden data are extracted for some legal considerations. There are different ways to embed reversible data in literature. Ni et al. [15] proposed a scheme of using peak/zero points in the histogram of spatial domain images. De Vleeschouwer et al. [13] proposed a circular histogram scheme and used the relative position of the center of mass of the zone-based histogram to convey the information bits. Fridrich et al. [3] used several methods to embed data. The central idea of their work is to compress the selected image features (e.g., the bit plane and the vector state in [3]) for acquiring spare space. The compressed original image features as well as their location information (also called location map) are embedded along with the payload. Fridrich et al. s prototype has influenced many other researches such as [2], where Celik et al. proposed a generalized-lsb data embedding method. Based on integer Haar wavelet transform, Tian [4] proposed another prototype using DE embedding. This prototype usually has larger embedding capacity and is easy to extend. In [16], a binary tree structure is used to eliminate the requirement to communicate pairs of peak and zero points to the received image, and a multilevel histogram shifting technique is adopted to prevent underflow and overflow. The histogram modification and shifting mechanism can also be implemented in the prediction error of host pixels [18], [19], and several good prediction approaches have been introduced to improve the performance of reversible data hiding [20], [21]. In [22] the optimal rule of value modification under a payload-distortion criterion is found by using an iterative procedure. 2. LOSSLESS GENERALIZED-LSB DATA EMBEDDING Mehmet Utku Celik,, Gaurav Sharma, Ahmet Murat Tekalp and Eli Saber [2] presented a reversible data-hiding technique. That technique enables the exact recovery of the original host signal upon extraction of the hidden information. A generalization of the wellknown least significant bit (G-LSB) modification is used as the data-embedding method. This method introduces additional operating points on the capacity-distortion curve. The reversible lossless recovery of the original is achieved by compressing portions of the host signal that are susceptible to embedding distortion and transmitting these compressed portion as a part of the embedding payload. A prediction-based conditional entropy coder which utilizes unaltered portions of the host signal as side-information improves the compression efficiency and, thus, the reversible data-embedding capacity. In this well-known method, the LSB of each signal sample is replaced (over written) by a payload data bit embedding one bit of data per input signal sample. If extra capacity is required, two or more LSBs may be
2 over written allowing for a corresponding bit per input signal sample. During extraction, these bits are reading in the same scanning order, and payload data is extracted. LSB modification is a simple, nonrobust embedding technique with a high-embedding capacity and small bounded embedding distortion (±1). A generalization of the LSB-embedding method, namely G-LSB, is presented here. If the original host signal is represented by a vector S, the G-LSB embedding and extraction processes can be represented as (1) (2) Where Sw represents the signal containing the embedded information, W represents the embedded payload vector of L-ary symbols, and (3) is an L -level scalar quantization function, and represents the Operation of truncation to the greater than or equal to integer part In G-LSB embedding, each signal sample carries an L ary Watermark symbol wi, which represents bits of information. Therefore, the embedding capacity of the system is (4) bits per sample (bps) barring boundary effects due to overflow restrictions. The lossless G-LSB embedding can achieve capacities exceeding 1 bpp, while RS Embedding s capacity performance is bounded from above by 1 bpp, with respect to the allowable distortion. The lossless G- LSB technique allows complete recovery of the original marked signal, and introduces only a small distortion between the host and image bearing the embedded data. The embedded capacity of the scheme depends on the statistics of the host image. The least significant bits (LSB) in a JPEG image can also be used to provide the required data space. In these reversible LSB data embedding methods, in order to accommodate secret data a spare place can always be made available as long as the chosen item is compressible, but the embedding capacities are not very high. The least significant digits of pixel values in an L- ary system or the least significant bits (LSB) of quantized DCT coefficients in a JPEG image can also be used to provide the required data space. In these reversible data hiding methods, a spare place can always to be made available to accommodate secret data as long as the chosen item is compressible, but the capacities are low. 3. REVERSIBLE DATA EMBEDDING USING A DIFFERENCE EXPANSION In the difference expansion method differences between two adjacent pixels are doubled so that a new LSB plane without carrying any information of the original is generated. The hidden message together with a compressed location map derived from the property of each pixel pair, But not the host information itself, is embedded into the generated LSB plane. Jun Tian [4] introduced a DE technique [DE], which discovers extra storage space by exploring the repetition bits in the image content. They employed the DE technique to reversibly embed a payload into digital host images. Both the payload embedding capacity limit and the visual quality of embedded images of the DE method are among the best in the literature, along with a computational complexity is low. Assume we have two values x=206, y=201, we would like to reversibly embed one bit b=1. First we compute the integer average l and difference h of x and y, Where the symbol is the floor function meaning the greatest integer less than or equal to. Next we represent the difference value h into its binary representation h=5= Then we append b into the binary representation of h after the least significant bit (LSB), the new difference value h will be h =101 b 2 = =11. Mathematically, this is equivalent to (6) (5)
3 Finally we compute the new values, based on the new difference value value, and the original integer average (7) From the embedded pair ( x, y ), we can extract the embedded bit b and restore the original pair ( x,y ). Again we compute the integer average and difference (8) Look into the binary representation of h, h =11= Extract the LSB, which is 1 in this case, as the embedded bit b, which leaves the original value of the difference as h=101 2 =5. Mathematically, this is equivalent to (9) Now with the integer average value l and restored difference value h, we can restore exactly the original pair ( x,y). In this example, we have embedded one bit b by increasing the valid bit length of the difference value h from 3 bits (for h=5) to 4 bits (for h =11). This reversible data-embedding operation is called the DE. (10) regions, the payload capacity limit of the G-LSB method does not exceed 1 bpp, while the DE method could easily embed more than 1 bpp. The payload capacity limit of the RS method is lower than both the G-LSB and the DE method. By embedding a payload of the same size (bit length), the embedded Lena image by the DE method is about 2 to 3 db higher than both the G-LSB and the RS method. However, this Difference Expansion method suffers from undesirable distortion at low embedding capacities and lack of capacity control due to the need for embedding a location map. 4. REVERSIBLE WATERMARK USING THE DIFFERENCE EXPANSION OF A GENERALIZED INTEGER TRANSFORM Adnan M. Alattar [5] used; a reversible watermarking algorithm with very high data-hiding capacity has been developed for color images. The algorithm hides several bits in the difference expansion of vectors of adjacent pixels. Also, the potential payload size capacity that can be embedded into a host image is discussed, and a feedback system for controlling this size is developed. The recursive embedding and embedding across color components to hide more data into a host image was used to hide more payloads. Spatial Triplets: A spatial triplet is a 1 X 3 or 3 X 1 vector formed from three consecutive pixel values in the same color component from the row- or column-wise, respectively. We applied this algorithm recursively to each color component: first to the columns and then to the rows. Spatial Quads: A spatial quad was assembled from 2 X 2 adjacent pixels in the same color component. We applied the algorithm to each color component independently. Fig. 1. Capacity versus distortion comparison of Lena. The capacity versus distortion comparison among the DE method, the G-LSB method, and the RS method on Lena is shown in Fig. 1. The top curve is the DE method. Except for images with large very smooth Cross-Color Embedding: To hide even more data, the algorithm can be applied across color components after it is applied independently to each color component either in row or column wise. In this case, the vector u contains the color components (R,G,B ) of each pixel arranged in a predefined order. This cross-color application can be done either as cross-color Triple u=(r,g,b), as cross-
4 color quad u=(r,g,g,b), or as a permutation thereof. The results indicate that the spatial, quad-based algorithm allows for hiding the largest payload at the highest signal-to-noise ratio. The amount of data can embed into an image depends highly on the nature of the image. This is the main drawback of this method. 5. DATA HIDING EXPLOITING SPATIAL CORRELATION BETWEEN SUB-SAMPLED IMAGES Reversible data hiding enables host media to be restored from marked media without any loss of original host information, because this reversibility algorithm helps to make a right decision during image analysis, it is highly desirable in quality-sensitive imagery where even the minimal distortion introduced by embedding data is unacceptable. K-.S.Kim, M.-J.Lee,H.-Y.Lee, and H.- K.Lee [17] used a reversible data hiding method that modifies the histogram difference between sub-sampled images of original host data. It exploits the high spatial correlation inherent in neighboring pixels to achieve high capacity and imperceptible embedding. pixels residue is neither included in any sub-sampled images nor targeted embeddable components by our data hiding scheme. The basic idea is utilize horizontal, vertical, and diagonal neighbors of a pixel since they have strongly spatial correlation and high pixel redundancy. A limitation of the presented algorithm is that it is hard to achieve more than 1 bpp because the only performed the first round of embedding, but the capacity of 1bpp is large enough for authentication purpose. Future work is to achieve multi round embedding by considering capacity- distortion performance. This section presents a histogram-based reversible data hiding method for images in spatial domain, which satisfies high embedding capacity, high visual quality, and low computational complexity. Sampling is the process of selecting units (e.g., pixels, coefficients) from an image. Suppose that an image of size N M pixels is denoted by I(x, y), where x = 0,...,M 1 and y = 0,...,N 1. Two sampling factors, Δu and Δv set the desired sub-sampling intervals in a row and column direction, respectively. This process is called as sub- sampling and each sub-sampled image Sk of size N/ Δ u M/ Δ v is obtained as follows: (11) Where i=0, M/ -1,j=0,.N/,and k=1,,. If N/ or M/ is not an integer value, we slightly modify the size of all sub-sampled images by flooring. For example, when M=N=512 and, the size of the sub-sampled images is set at the nearest integer less than or equal to the number 512/3 = 170 in both row and column. The Fig 2: Comparison of actual embedding capacity in bpp versus distortion with other reversible schemes The embedding capacity means EC where the amount of over- head information is extracted. That is, peak values for blocks, an original peak and a zero point in the image histogram, etc.. DE scheme and G-LSB scheme showed relatively high embedding capacity versus the allowable PSNR value, whereas the RS scheme had low embedding capacity compared to others. The histogram-based Nietal.'s scheme showed the fixed 48.2dB pixel PSNR quantity, but the capacity achievement was a little varied for each and every images. The embedding performance of the Linetal.'s scheme did not achieve more than 0.3 bpp for the test images, because the amount of the peak information for all blocks exceeded the whole embedding capacity when the so-called hiding level was much higher up. In other words, it suffered from the lack of capacity control due
5 to the need for embedding all peaks information for blocks. Experimental results support that the difference histogram modification algorithm achieves high embedding capacity with low distortions and out performs other reversible data hiding schemes. 6. REVERSIBLE DATA HIDING WITH OPTIMAL VALUE TRANSFER In [22] Xinpeng Zhang, used the practical reversible data hiding scheme for perfectly restored the original content after extraction of the hidden data. That is the optimal rule of value modification under a payload-distortion criterion is found by using an iterative procedure. The secret data, as well as the auxiliary information are carried by the differences between the original pixel-values and the corresponding values estimated from the neighbors. Here, the optimal value transfer rule is used to modify the estimation errors. The optimal value transfer matrix is produced for maximizing the amount of secret data, i.e., the pure payload, by the iterative procedure. Also, the host image is divided into a number of pixel subsets and an estimation error in the next subset is always embedded by the auxiliary information of a previous subset. A receiver can successfully extract the embedded secret data and recover the original content in the subsets with an reverse order. In this way, a good reversible practical data hiding performance is achieved. This way, a good payload-distortion performance can be achieved. In other words, the optimal transfer mechanism gives a new rule of value modification and can be used on various cover values. A better performance is achieved, if a smarter prediction method is exploited to make the estimation errors closer to zero, but the computation complexity is higher due to the prediction and iterative procedure. This is the major drawback of this method. 7. CONCLUSION In this paper, a brief literature survey on performance of reversible data hiding algorithm is discussed. From this study it is concluded that the difference histogram shifting algorithms provide better performance than other conventional algorithms. ACKNOWLEDGEMENT The success of my work depends on the supports and guidelines of others. I take this opportunity to express my gratitude to the people who have been supported me in the successful completion of this work. I owe sincere prayer to the LORD ALMIGHTY for this kind blessing without which this would not have been possible. I wish to take this opportunity to express my gratitude to all who have helped me directly or indirectly to complete this paper. REFERENCES [1] M. Goljan, J. Fridrich, and R. Du, Distortion-free data embedding, in Proc. 4th Int. Workshop on Information Hiding, Lecture Notes in Computer Science, 2001, vol. 2137, pp [2] M. U. Celik, G. Sharma, A. M. Tekalp, and E. Saber, Lossless generalized- LSB data embedding, IEEE Trans. Image Process., vol. 14,no. 2, pp , Feb [3] J. Fridrich, M. Goljan, and R. Du, Lossless data embedding for all image formats, in Proc. Security and Watermarking of Multimedia Contents IV, Proc. SPIE, 2002, vol. 4675, pp [4] J. Tian, Reversible data embedding using a difference expansion, IEEE Trans. Circuits Syst. Video Technol., vol. 13, no. 8, pp , Aug [5] A. M. Alattar, Reversible watermark using the difference expansion of a generalized integer transform, IEEE Trans. Image Process., vol.13, no. 8, pp , Aug [6] X. Wang, X. Li, B. Yang, and Z. Guo, Efficient generalized integer transform for reversible watermarking, IEEE Signal Process. Lett.,vol. 17, no. 6, pp , [7] H.-C. Wu, C.-C. Lee, C.-S. Tsai, Y.-P. Chu, and H.- R. Chen, A high capacity reversible data hiding Scheme with edge prediction and difference expansion, J. Syst. Softw., vol. 82, pp , [8] D.M. Thodi and J. J. Rodríguez, Expansion embedding techniques for reversible watermarking, IEEE Trans. Image Process., vol. 16, no. 3,pp , Mar [9] L. Kamstra and H. J. A. M. Heijmans, Reversible data embedding into images using wavelet techniques and sorting, IEEE Trans. Image Process., vol. 14, no. 12, pp , Dec [10] H. J. Kim, V. Sachnev, Y. Q. Shi, J. Nam, and H.- G. Choo, A novel difference expansion transform for reversible data embedding, IEEE Trans. Inf. Forensics Security, vol. 3, no. 3, pp , 2008.
6 [11] S. Weng, Y. Zhao, J.-S. Pan, and R. Ni, Reversible watermarking based on invariability and adjustment on pixel pairs, IEEE Signal Process. Lett., vol. 15, pp , [12] Y. Hu, H.-K. Lee, and J. Li, DE-based reversible data hiding with improved overflow location map, IEEE Trans. Circuits Syst. Video Technol., vol. 19, no. 2, pp , Feb [13] C. Vleeschouwer, J.-F. Delaigle, and B. Macq, Circular interpretation of bijective transformations in lossless watermarking for media asset management, IEEE Trans.Multimedia, vol. 5, no. 1, pp ,2003. [14] Z. Ni, Y. Q. Shi, N. Ansari,W. Su, Q. Sun, and X. Lin, Robust lossless image data hiding designed for semi-fragile image authentication, IEEE Trans. Circuits Syst. Video Technol., vol. 18, no. 4, pp ,Apr [15] Z. Ni, Y.-Q. Shi, N. Ansari, and W. Su, Reversible data hiding, IEEE. Circuits Syst. Video Technol., vol. 16, no. 3, pp , Mar [16] W.-L. Tai, C.-M.Yeh, and C.-C.Chang, Reversible data hiding based on histogram modification of pixel differences, IEEE Trans. Circuits Syst. Video Technol., vol. 19, no. 6, pp , Jun [17] K.-S. Kim, M.-J. Lee, H.-Y. Lee, and H.-K. Lee, Reversible data exploiting spatial correlation between sub-sampled images, Pattern Recognit., vol. 42, pp , [18] C.-C. Chang, C.-C. Lin, and Y.-H. Chen, Reversible data-embedding scheme using differences between original and predicted pixel values, IET Inf. Security, vol. 2, no. 2, pp , [19] P. Tsai, Y.-C. Hu, and H.-L. Yeh, Reversible image hiding scheme using predictive coding and histogram shifting, Signal Process. vol. 89, pp , [20] W. Hong, T.-S. Chen, Y.-P. Chang, and C.-W. Shiu, A high capacity reversible data hiding scheme using orthogonal projection and prediction errormodification, Signal Process., vol. 90, pp , [21] L. Luo, Z. Chen, M. Chen, X. Zeng, and Z. Xiong, Reversible image watermarking using interpolation technique, IEEE Trans. Inf. Forensics Security, vol. 5, no. 1, pp , [22] Xinpeng Zhang, Reversible Data Hiding With Optimal Value Transfer, IEEE Transaction on multimedia, vol. 15, no. 2 february 2013
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