V. Kavitha 1 M. Subhashree 2 M.E II Year (Computer Science Engineering) Assistant Professor, Department of CSE,

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1 International Conference on Current Research in Engineering Science and Technology (ICCREST-2016) Detection And Recovery Of Tampered Images V. Kavitha 1 M. Subhashree 2 M.E II Year (Computer Science Engineering) Assistant Professor, Department of CSE, Peri Institute of Technology Peri Institute of Technology Chennai Chennai Abstract- Image security is a great concern in transferring an image through a wireless network. When images are sent through wireless networks there is a chance for the hacker to modify the image. The purpose is to detect the tamper of received image and recover the lost information in the tampered zones. Check bits are used for tamper detection. DWT algorithm is used for image compression. Permutation process interchanges the image after the grayscale conversion. Channel encoding and decoding algorithm performs adding reference bits and detecting the tampered bits. The proposed scheme outperforms recent techniques in terms of image quality for recovered image. The received image is being checked against tamper and self recovered appropriately. Keywords- Watermarking, RS code, tampering protection, self-recovery. I.INTRODUCTION Image processing is a method to convert an image into digital form and perform some operations on it, in order to get an enhanced image or to extract some useful information from it. The two types of methods used for Image Processing are analog and digital image processing. Analog or visual techniques of image processing can be used for the hard copies like printouts and photographs. We use digital image processing method in which algorithms are used to perform image processing on digital images. In our paper we develop a scheme to detecting the tampered area of the received image and recovering the lost information in the tampered zones. Check bits are used for tampering detection, whereas reference bits carry information about the whole image. In watermark embedding phase, the original image is source coded and the output bit stream is protected using appropriate channel encoder. For image recovery, erasure locations detected by check bits help channel erasure decoder to retrieve the original source encoded image. The watermarked image quality gain is achieved through spending less bit-budget on watermark, while image recovery quality is considerably improved as a consequence of consistent performance of designed source and channel codes. II.RELATED WORKS Two major recent image self-recovery works with which our proposed method is compared are discussed briefly in this section. Zhang et. al. propose an image self-embedding method based on DCT coefficients of the image. The sparse DCT coefficients of the image blocks are then under sampled using a pseudorandom matrix satisfying the restricted isometry property (RIP) required for the compressive sensing and sparse processing. The resulting projected values are then non-uniformly quantized and embedded as the watermarked bits. The reference data lost due to tampering is recovered at the receiver either using a compositive reconstruction approach depending on whether the amount of the surviving reference data is below or above a certain limit. The reference data is generated by the least square quantization of the DCT coefficients. This information is then channel coded with the rate λ and embedded as the watermark data. Therefore, the λ parameter determines the trade-off between the quality of the restored image and TTR for a certain embedding capacity. The higher λ values mean lower channel code protection and hence lower TTR. However, in this case the embedding E-ISSN : Page 57

2 capacity is dedicated more to the reference data and the lost data will be recovered with a higher quality while the tampering rate is below TTR. On the other hand, the embedding capacity is rather dedicated to the channel coding parity bits than reference bits for smaller λ cases. III.PROPOSED MODEL A. Image Compression In the initial step the image will be uploaded. To design a image to 8-bit form the DWT algorithm will be used.this is a type of wavelet transform. The wavelet transform and set partitioning in hierarchical transforms (DWT) source encoding method to efficiently compress the original image. Therefore, the watermark consists of three parts in our algorithm: source code bits, channel code parity bits and check bits. Source code bits which act as the reference bits are the bit stream of the DWT -compressed original image at a desired rate. In order to survive tampering erasure, the reference bits are channel coded to produce channel code bits. Check bits are used at the receiver to determine the erasure location for the channel erasure decoder. The output of channel decoder is source decoded to find the compressed version of the original image. This work shows that by choosing appropriate parameters for source and channel encoding, our algorithm outperforms existing methods in the same watermark payload of three bits per pixel. B. Permutation Prior to the permutation process gray scale conversion of the image is done. Image is converted into grayscale image. The compressed image is converted to gray scale image in order to enhance the security of the compressed image. Then the gray scale conversion is followed by the permutation process. Permutation means interchange the value of x and y axis. The images is to be changed then bit value is to be inserted into reference bits and then secret key is converted into hash code, those values are stored in reference bits. Channel coding algorithm is to be used to add the reference bits values. Channel decoder having information about this reference bits. The source channel code design and having error locations is to be noted. It Re-permutates the image and then it sends to receiver. C. Tampering Once the image has undergone compression and permutation process the work of the sender is over and the secured image is ready to be transferred. The image is then forwarded to the desired receiver. The image from the sender can be sent through various available paths. There are many hackers in the network who can even hack the data s or images in a trusted path. The hackers initially trace and find the path of transmission of the vital images. Then they choose their desired technique for hacking the image. The hacker may modify most of the parts of the image or only some parts of the image. The modification can be of any form.the image will be modified in such a way that it does not differ from the original image in any manner. D. Detection and Recovery Tampered image is detected and selfrecovered using channel RS encoding and decoding algorithm. Image is to be calculated hash bits and extracted check bits is recorded for each block. Therefore, comparing these results and spotting the different ones leads to locating the tampered blocks. After locating the tampered blocks, Channel code bits undergo proper inverse permutation. The compressed image bit stream available at the output of the decoder is passed through the source decoder after undergoing proper inverse permutation. The reconstructed image is made by replacing the tampered blocks by their corresponding blocks at the output of the source decoder. Obviously, the content of the received image in preserved blocks will not be replaced with the corresponding information which are being derived from the restored image. IV. DISCRETE WAVELET TRANSFORM ALGORITHM Discrete Wavelet Transform (DWT) is any wavelet transform for which the wavelets are discretely sampled. It transforms discrete time signal to discrete wavelet representation, by doing this some unwanted internal details of the image can be removed for compressing. Image compression is a key technology in transmission and storage of digital images because of vast data associated with them. The objective of image compression technique is to reduce redundancy of [Type text] Page 58

3 the image data in order to be able to store or transmit data in an efficient form. This results in the reduction of file size and allows more images to be stored in a given amount of disk or memory space. Image compression can be lossy or lossless. In a lossless compression algorithm, compressed data can be used to recreate an exact replica of the original; no information is lost to the compression process. In lossy compression, the original signal cannot be exactly reconstructed from the compressed data. Compression Method using DWT, first decomposes an image into coefficients called sub-bands and then the resulting coefficients are compared with the International Journal of Computer Science Issues, Vol. 9, Issue 4, No 1, July 2012 threshold. Coefficients below the threshold are set to zero. Finally, the coefficients above the threshold value are encoded with a loss less compression technique. The compression algorithm based on DWT is comprised of three steps, and the steps are given as follows: 1.Decompose Choose a wavelet; choose a level N. Compute the wavelet. Decompose the signals at level N. 2.Threshold detail coefficients for each level from 1 to N, a threshold is selected and hard thresholding is applied to the detail coefficients. 3.Reconstruct Compute wavelet reconstruction using the original approximation coefficients of level N and the modified detail coefficients of levels from 1 to N. L Low frequency sub band H High frequency sub band Fig. 1. Example of DWT compression V. CHANNEL RS ALGORITHM Reed-Solomon codes are block-based error correcting codes with a wide range of applications in digital communications and storage. Reed Solomon codes are used to correct errors in many The Reed-Solomon encoder takes a block of digital data and adds extra "redundant" bit. Errors occur during transmission or storage for a number of reasons The Reed-Solomon decoder processes each block and attempts to correct errors and recover the original data. The number and type of errors that can be corrected depends on the characteristics of the Reed-Solomon code. V. CHANNEL RS ALGORITHM Reed-Solomon codes are block-based error correcting codes with a wide range of applications in digital communications and storage. Reed- Solomon codes are used to correct errors in many The Reed-Solomon encoder takes a block of digital data and adds extra "redundant" bit. Errors occur during transmission or storage for a number of reasons (for example noise or interference, scratches on a CD, etc). The Reed-Solomon decoder processes each block and attempts to correct errors and recover the original data. The number and type of errors that can be corrected depends on the characteristics of the Reed- Solomon code. The general form of the generator polynomial is: g(x) = x a x a (x a ) (1) and the codeword is constructed using: c(x) = g(x). i(x) (2) [Type text] Page 59

4 Where, g(x) is the generator polynomial, i(x) is the information block, c(x) is a valid codeword and is referred to as a primitive element of the field. Encoding: The 2t parity symbols in a systematic Reed-Solomon codeword are given by: p(x) = i(x). xx k mod g(x) (3) Each of the 6 registers holds a symbol (8 bits). The arithmetic operators carry out finite field addition or multiplication on a complete symbol. Decoding: A general architecture for decoding Reed-Solomon codes consists of the following components. The components are given either as input of succeeding blocks in the decoder or the components are the output of certain blocks. r(x) = c(x) + e(x) (4) The received codeword r(x) is the original codeword (transmitted codeword) c(x) plus errors. Fig. 2. (a) Watermarked image is tampered (b) Tampered area detected by check bit examination. (c) The original image recovered from the tampered. IEEE Trans. Image Process., vol. 11, no. 6, pp VI.CONCLUSION [2]. Fridrich J. (1998), Image watermarking for tamper detection, in Proc.(ICIP), vol.2., pp In this paper, we introduced a watermarking scheme to protect images against tampering. The original image is source coded using channel RS encoding algorithm. The image quality is enhanced by compressing the image using DWT algorithm and the image is secured by using permutation process where the axis being interchanged. The permutated image is tampered by the hacker before it reaches the receiver end. Once the tampered image is received it is repermutated and then checked against tamper. The tampered zone is detected using the channel RS decoding algorithm. Therefore, the receiver knows the exact location of erroneous bits. So tampered image can be detected and self- recovered. REFERENCES [1]. Celik M.U., Sharma G., Saber E. and Tekalp A.M. (2002), Hierarchical watermarking for secure image authentication with localization, [3]. Kundur D. and Hatzinakos D. (1999), Digital watermarking for telltale tamper proofing and authentication, Proc. IEEE, vol. 87, no. 7, pp [4]. Lu C.S., Huang S.K., Sze C.J. (2000), Cocktail watermarking for digital image protection, IEEE Trans. Multimedia, vol. 2, no. 4, pp [5]. Roy S. and Sun Q. (2007), Robust hash for detecting and localizing image tampering, in Proc. IEEE(ICIP), vol.6, pp. VI [6]. Suthaharan S. (2003), Fragile image watermarking using a gradient image for improved localization and security, Pattern Recognit. Lett., vol. 25. [Type text] Page 60

5 [7]. Swaminathan A., Mao Y. and Wu M. (2006), Robust and secure image hashing, IEEE Trans. Inf. Forensics Security, vol. 1, no. 2, pp [8]. Tagliasacchi M., Valenzise(2009), Hashbased identification of sparse image tampering, IEEE Trans. Image Process, vol. 18, no.11, pp [9]. Wong P.W. and Memon N. (2001), Secret key image watermarking schemes for image authentication.ieee Trans. Image Process, vol. 10, no. 10, pp [10]. Wu M. and Liu B. (1998), Watermarking for image authentication, in Proc.Int. Conf. Image process. (ICIP), vol , pp