ABSTRACT: This paper deals about employing wavelet based and SVD based watermarking for image forensics. The paper proposes ways of tampering detection from the watermark without actually using the original image (blind detection). This computation is done based on the positional values for the wavelet based watermarking and using the singular plane in the SVD method. We detect whether tampering has been done using the Wavelet based technique. We also show how to recover the original image data even from the tampered image with the retrieved watermark in the SVD method. Practical implementations of all the above techniques are done using MATLAB. Keywords—Image forensics, Tampering detection, Watermark based forensics, SVD, Blind detection.

1. INTRODUCTION

mages and videos represent a common source of evidence. The trustworthiness of photographs thus has an essential role in many areas, including: forensic

investigation, criminal investigation, surveillance systems, intelligence services, medical imaging, and journalism. The art of making image fakery and tampering has a long history. Image processing experts can easily access and modify image content, and therefore its meaning, without leaving visually detectable traces as shown in fig.1 [1] and fig.2 [8]. One of the primary goals of digital image forensics is authentication of images and image regions which have undergone some form of manipulation or alteration. Because of the ill-posed nature of this problem, no catchall method of detecting image forensics exists. Instead, a number of techniques have been proposed to identify image alterations under a variety of scenarios. While each of these methods possess their own limitations. Generally, these approaches could be divided into active and passive-blind approaches [8]. The area of active methods simply can be divided into the data hiding approach and the digital signature approach. By data hiding we refer to methods embedding secondary data into the image. The most popular group of this area belongs to digital watermarks. Digital watermarking assumes an inserting of a digital watermark at the source side (e.g., camera) and verifying the mark integrity at the detection side. Passive blind methods includes finding out forgery without any pre information about original image. For example SIFT based method [4], PCA based methods.

(a) Original Image (b) Tampered Image

Fig.1 Courtesy: Hailing Huang, Weiqiang Guo, Yu Zhang, “Detection of Copy-Move Forgery in Digital Images Using SIFT Algorithm”, 2008 IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application [1]

Fig. 2 The doctored image depicting Jeffrey Wong Su En while receiving the award from Queen Elizabeth II, published in Malaysian dailies, and the original picture of Ross Brawn receiving the Order of the British Empire from the Queen (b) [8] Digital watermarking is one of the techniques used for tampering detection. Watermarking schemes may be classified into two groups, spatial domain and transform domain watermarking schemes. Spatial domain schemes embed messages in pixels of an image directly. The least significant bit (LSB) scheme is the most common and easiest method for embedding messages in an image. But the disadvantage is its lower security and proneness to distortion. For transform domain methods , images are transformed to frequency domain, and then messages are embedded into the frequency coefficients, such as discrete Fourier transform (DFT), discrete cosine transform (DCT), and discrete wavelet transform (DWT). Usually, the transform domain scheme is more robust to resist image processing attacks than spatial domain.

Sarika Bhosale,Ganesh Thube,Pooja Jangam Mr.Rushikesh Borse Students of E&TC Engineering, Assistant Professor, Department of E&TC Engineering Sinhgad Academy of Engineering, Pune University, Sinhgad Academy of Engineering,Pune University,

Pune-48. Pune-48

Employing SVD and Wavelets for Digital Image Forensics and Tampering Detection

I

2012 International Conference on Advances in Mobile Network, Communication and Its Applications

978-0-7695-4720-6/12 $26.00 © 2012 IEEE

DOI 10.1109/MNCApps.2012.35

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1.1 MOTIVATION: In today’s digital age, it is possible to change the information represented by an image without leaving any obvious traces of tampering. The digital information revolution and issues concerned with multimedia security have also generated several approaches to detect tampering, but none of the method applicable for all types of forgeries. So our aim is to find out such methods that will serve best for any type of attack. 1.2 CONTRIBUTION: This work focuses on blind methods [7], as they are regarded as a new direction and in contrast to active methods they do not need any prior information about the image. Blind methods are mostly based on the fact that forgeries can bring into the image specific detectable changes (e.g., statistical changes). In high quality forgeries, these changes cannot be found by visual inspection.

2. REVIEW OF EXISTING TECHNIQUES

There are many methods that detect digital image tampering. But none of the method detects all types of forgeries. Detection of near-duplicated image regions may signify copy–move (copy-paste) forgery [9]. In this type of forgery, a part of the image is copied and pasted into another part of the same image typically with the intention to hide an object or a region. For such a type of copy–move forgery detection method has been proposed by Fridrich et al. [1,2,3,4]. The detection of duplicated regions is based on matching the quantized lexicographically sorted discrete cosine transform (DCT) coefficients of overlapping image blocks. The lexicographically sorting of DCT coefficients is carried out mainly to reduce the computational complexity of the matching step. The next one is SIFT based technique [1]. Hailing Huang et al. used the SIFT algorithm to detect the cloned regions in the image. SIFT features are stable with respect to changes in illumination, rotation and scaling. Babak Mahdian and Stanislav Saic [2] proposed a method for detecting near-duplicated regions based on moment invariants, principal component analysis and kd-tree. Although these methods are capable of detecting near duplicates parts of the image, their computational time is very high and typically they produce a high number of false positives. Furthermore, a human interpretation of the results is necessary.

3. PROPOSED WORK

(a) Wavelet Based Method:

Due to the fact that spatial domain watermarking scheme is vulnerable to image processing attacks. Hence, this proposed scheme modifies the original image in transform domain first and embedding a watermark in the difference values between the original image and its reference image to overcome the weak robustness problem [5] in spatial domain. Moreover, the watermark extraction does not require the original image so the application is more practical in real life for ownership verification.

Watermark Embedding:

Fig.3 Watermark embedding process.

We implement this scheme by using Joo et al.’s scheme for single level wavelet decomposition of the given image. The original image X is a gray-level image with M by N pixels. X can be defined as: � � ����� �� � � � ��� � � ��� ���� ����

…… (1) Firstly, the image with size of M by N pixels is transformed into wavelet coefficients by single level wavelet transform (as shown in Fig. 3).Three high frequency sub-bands (LH1, HL1, and HH1) are set to zero. Then after taking inverse wavelet transform, it’s reference image �� is obtained. The information idx of embedding location in the watermark embedding process is obtained by sorting �� � ���. For that compute the differences between the original image � and its reference image���. Then obtain location idx(i,j) such that � � ����� � ����� � � �, where s and t ����. The watermark � is a pseudo random bit-sequence. They are defined as follows: � � ��� �� !��� � ��� ���� …… (2)

Finally, the watermark information is embedded into the sub-band � by � �� " # ���$��� , where k is a factor for controlling embedding intensity. Now randomly select some locations to embedding. The watermark is embedded as follows.

��%�$���� & �� ��'()(* �����$���� + ,������������������������������ � ��-!$����������������������������������������������������������������� � . � ��������/��$���� � ,������������������������������ � ���-!$����������������������������������������������������������������� � 0 � �

…… (3)� �1232� 0 � ���%�$���� & � �%�$���� & ������������������������������������������������. � �%�$���� & � ����$���� ��

����Here���, � 345!$ 67�89 :, this is the visual weight to balance the tradeoff between the robustness and imperceptibility. The sequence idx of embedding locations should be kept as the secret key for subsequent watermark extraction.

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Fig.4 Original Image Fig.5 key points Watermark extraction and tampering detection: In watermark extraction process, according to the embedding location, the watermark can be extracted by comparing the � and �/. Finally, the extracted watermark is compared with the original watermark and tampering can be detected from it. The watermark extraction [5] process does not require the original image. We utilize the sequence of embedding location to extract the watermark. Exactly same process followed at the receiver side. First of all transform the watermarked image into wavelet coefficients by one-level wavelet transform. Set three high-frequency sub-bands (LH1, HL1, and HH1) as zero. Perform the inverse wavelet transform and obtain its reference image �/;�< According to the sequence of embedding location, obtain the embedded watermark bits by the following formula.

�� � = �� �;��$���� > ��;��$���� ��� �;��$���� � ��;��$���� …… (4)

Now compare the extracted watermark W’ with the original watermark W for trustworthiness.

(b) SVD based method:

This method is based on the singular value decomposition[4].Suppose M is an mxn matrix whose entries come from the field K, which is either the field of real numbers or the field of complex numbers. Then there exists a factorization of the form

M=USV* …… (5)

Where U is an mxn unitary matrix over K, the matrix S is an mxn diagonal matrix with nonnegative real numbers on the diagonal, and V*, an mxn unitary matrix over K, denotes the conjugate transpose of V. Such a factorization is called the singular value decomposition of M. The diagonal entries �i of S are known as the singular values of M. A common convention is to list the singular values in descending order. In this case, the diagonal matrix S is uniquely determined by M (though the matrices U and V are not).

Watermark Embedding process:

Initially perform the discrete wavelet transform (DWT) of the given image X (as shown in fig.3). Then calculate the discrete cosine transform (DCT) of the approximated discrete wavelet transform obtained. Now perform the singular value decomposition on the original image and obtain matrices U, S and V. Add Watermark in matrix S (as shown in fig.6). Discrete cosine transform (DCT) coefficients are used as watermark.

Fig.6 Embedding watermark

Now insert watermark in the original image by the following equation: ?@�AB � ? + , # �AB@�-� …… (6)

Where, S is singular matrix S_img is Watermarked added matrix img_wat is watermark to be added , is the extent of watermarking depending on the imperceptibility.

Again perform the singular value decomposition on S_img and obtain the three matrices in order to obtain watermarked image by U, S_img and V matrix multiplication as shown in equation(7). �@�AB � C # ?@�AB # D/ …… (7)

Fig.7 S matrix + watermark and Watermarked image Watermark extraction and tampering detection: Now in order to detect whether image has been tampered or not, we have to extract watermark from received image (as shown in fig.8) for that perform the singular value

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decomposition on the received image W_img. Extract Watermark from S matrix by using following formula: E2F@�AB � �@�AB � �?@�ABG, …… (8)

Fig.8 Watermark Extraction

Once we get watermark take inverse discrete cosine transform (DCT) of that watermark to obtain approximate coefficient of original image. Approximate coefficients effectively represent original image. Now compare this image with received image to detect whether forgery has been done or not.

4. EXPERIMENTAL RESULTS

Fig.9 Tampered Image and Recovered Image

5. CONCLUSION

• In wavelet based method we compare extracted

watermark with transmitted watermark. This method only detects whether forgery has been done or not.

• In wavelet based method we cannot recover the content of original image. This drawback can be removed by SVD based method for tampering detection.

• As watermark is DCT of original image we can get original image back. By comparing this image with received image we can easily find out what crime has been done.

• Watermarking in transform domain gives more robustness to the technique and it can detect attacks on the image like copy and move forgeries. The transform domain technique gives more reliability and is less prone to attacks.

• The singular value decomposition technique provides the spatial location of the part of image that has been tampered by comparing the recovered image and the tampered image that the receiver receives.

6. FUTURE SCOPE The first approach shows whether forgery has been detected or not where as the second approach gives the area which is tampered or suffered from the attack. First technique is not sufficient to detect the attacks on the image. The technique which works well in case of detecting a kind of attack may fail in case of attacks of other kind. So, the universal method for image tampering detection can give rise to new era in the image tampering detection algorithms. Error concealment and recovering the tampered data is the new gate for the research.

ACKNOWLEDGMENT

Authors are thankful to Mr.V.K.Bairagi and Mr.S.D.Ruikar for their valuable suggestions and constructive comments.

REFERENCES

[1] J. Fridrich, D. Soukal, and J. Lukas.“Detection of copy–move forgery in digital images”. In Proceedings of Digital Forensic Research Workshop, Cleveland, OH, USA, IEEE Computer Society. August 2003. [2] B. Mahdian, S. Saic, “Detection of copy–move forgery using a method based on blur moment invariants”, Forensic Science International, 2007. [3] J. Fridrich, D. Soukal, J. Lukas, “Detection of copy–move forgery indigital images”, in: Proceedings of Digital Forensic Research Workshop, 2003. [4] H. Huang, W. Guo, Y. Zhang, “Detection of copy–move forgery in digital images using SIFT algorithm”, in Proceedings of the 2008 IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application, 2008. [5] Jiang-Lung Liu, Der-Chyuan Lou *, Ming-Chang Chang, Hao-KuanTso,” A robust watermarking scheme using self-reference image”,in: Computer Standards & Interfaces 28 (2006) 356– 367. [6] Dr. Edel Garcia, “Singular Value Decomposition (SVD)-A Fast Track Tutorial”,September 11, 2006. [7] Babak Mahdian* , Stanislav Saic,” A bibliography on blind methods for identifying image forgery”, Signal Processing: Image Communication 25 (2010) 389–399. [8] Judith A. Redi ,Wiem Taktak & Jean-Luc Dugelay, “Digital image forensics: a booklet for beginners”, Published online: 24 October 2010.This article is published with open access at Springerlink.com. [9] Liu Zhulong , Li Xianghua & Zhao Yuqian , “Passive Detection of Copy-paste Tampering for Digital Image Forensics”, Fourth International Conference on Intelligent Computation Technology and Automation,2011.

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