Image Splicing Detection involving Moment-based Feature Extraction and Classification using Artificial Neural Networks

Image Splicing Detection involving Moment-based Feature Extraction and Classification using Artificial Neural Networks

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  ACEEE Int. J.on Signal & Image Processing, Vol. 01, No. 03, Dec 2010   Image Splicing Detection involving Moment-based Feature Extraction and Classification using Artificial Neural Networks K.Anusudha1 , Samuel Abraham Koshie2, S.Sankar Ganesh3 and K.Mohanaprasad4 School of Electronics Engineering (SENSE) VIT University, Vellore, India E-mail: anusudhak@yahoo.co.in,sankar_smart@rediffmail.com,mohanme2006@yahoo.co.in Abstract - In the modern age, the digital image has taken The rest of this paper is organized as follows. Section 2 the place of the original analog photograph, and the forgery discusses the theory used for the detection of spliced of digital images has become increasingly easy, and harder images. In section 3, the methodology used for extraction to detect. Image splicing is the process of making a of features is presented. Extraction of the features and composite picture by cutting and joining two or more subsequent training and classification using Neural photographs. An approach to efficient image splicing detection is proposed here. The spliced image often Networks is discussed. Later in section 4, the discussion introduces a number of sharp transitions such as lines, of the experimental results is presented. Finally edges and corners. Phase congruency is a sensitive measure conclusions are drawn in section 5. of these sharp transitions and is hence proposed as a feature for splicing detection. Statistical moments of II. PROPOSED METHODOLOGY characteristic functions of wavelet sub-bands have been examined to detect the differences between the authentic The splicing detection can be considered as a images and spliced images. Image splicing detection can be two-class pattern recognition problem. The input images treated as a two-class pattern recognition problem, which are categorized into two classes: spliced image and non- builds the model using moment features and some other spliced (authentic) image. parameters extracted from the given test image. Artificial A. Moments of characteristic function neural network (ANN) is chosen as a classifier to train and test the given images. Image histogram has been widely used in image analysis. Any pmf ‘px’ may be expressed as a probability Keywords: Image splicing, phase congruency, statistical density function (pdf) ‘fx by using the relation moments, characteristic functions, wavelet decomposition, artificial neural network (ANN) (1) Histogram of an image (or its wavelet I. INTRODUCTION subbands) and its CF is denoted by h(fi) and H(fk), Image splicing, as its name implies, is a simple respectively.The nth moment of the CF is defined as process of cropping and pasting regions from the same or follows. different images to form another image without post- processing such as edge smoothing. Image splicing (2) detection is hence urgently called for digital data where H(fj) is the CF component at frequency fj, N is the forensics and information assurance. People need to total number of points in the horizontal axis of the know if a given image is spliced or not without any a histogram. priori knowledge[1,2]. B. Prediction – Error Image In this paper, a novel approach to image splicing detection by exploiting the magnitude and phase The prediction-error image is the difference information of a given test image is proposed. It is between the test image and its predicted version. The proposed to use the moments of wavelet characteristic prediction algorithm is given below. function as one part of the image features to detect the spliced images. (3) where a, b, c are the context of the pixel x under considerations, xˆ is the prediction value of x. 9 © 2010 ACEEE DOI: 01.IJSIP.01.03.5  
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  ACEEE Int. J.on Signal & Image Processing, Vol. 01, No. 03, Dec 2010   features are extracted from prediction-error image. The additional 42 dimensional features are collected from C. 2-D Phase congruency three reconstructed images. To generate the The image splicing leaves traces of image reconstructed image I0i (i = 1, 2, 3) from the test image, manipulation especially at locations where sharp image Discrete Daubechies wavelet transform is applied on the transition is introduced. Phase congruency (PC) was first test image. In other words, I01, I02, and I03 are generated defined by Morrone and Owens [9] in terms of the by erasure of approximation subband LL1, LL2 and LL3, Fourier series expansion of a signal at some location x as respectively. E. Neural network classifier In this paper, an artificial neural network (4) (ANN) [11], specifically, a perceptron, which is the where An is the amplitude of the nth Fourier component, simplest kind of feedforward neural network is used as is the local phase of the nth Fourier component at a linear classifier. position x, and is the amplitude weighted mean local The Perceptron is a binary classifier that maps phase angle at position x. Let the image be denoted by its input x (a real-valued vector) to an output value f(x) (a I(x, y), the even-symmetric filter and odd-symmetric single binary value) across the matrix. filter at scale n and orientation ‘o’ is denoted by and , respectively. The responses of each quadrature pair of filters are a vector: (10) where w is a vector of real-valued weights and is the dot product (which computes a weighted sum). b is (5) the 'bias', a constant term that does not depend on any where * is the convolution operator. From Equation (5), input value. the amplitude of this response is given by III. IMPLEMENTATION (6) The image dataset is collected from DVMM, and phase is given by Columbia University. It consists of 933 authentic and 912 spliced image blocks in which all the image is of size 128x128. Examples of authentic and spliced image (7) are shown in Figures 1 and 2 below respectively. More The 2-D phase congruency is then calculated by details about the image sets can be found in [4]. (8) Where denotes that the enclosed quantity is equal to itself if it is positive, and equal to zero otherwise; is a measure of significance of frequency spread; ε is a small positive constant used to prevent division of zero; To is a quantity introduced to compensate image noise; and is a sensitive phase deviation function defined as Figure 1. Examples of authentic images (9) D. Feature extraction procedure The first 78 dimensional features are collected from the test image I and its prediction-error image Î.For each wavelet subband, the first three moments are derived according to Equation (2), resulting in 39 dimensional features. Similarly, another 39 dimensional 10 © 2010 ACEEE DOI: 01.IJSIP.01.03.5  
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  ACEEE Int. J.on Signal & Image Processing, Vol. 01, No. 03, Dec 2010   Original image 1-level reconstructed image 2-level reconstructed image 3-level reconstructed image Figure 2. Examples of spliced images TABLE 1 - Numbers of image blocks in different subcategories Figure 4. Three-level wavelet reconstruction of original image One One Textured- Textured- Smooth- Predicted image 1-level reconstructed image Textured Smooth Smooth Textured Smooth Category Background Background Interface Interface Interface (T) (S) (TS) (TT) (SS) Authentic 126 54 409 179 165 (Au) Spliced (Sp) 126 54 298 287 147 2-level reconstructed image 3-level reconstructed image A. Extracting features from an image Figure 5. Three-level wavelet reconstruction of predicted image A.3. Prediction –Error Image Figure 3. Original Image Step1: Using the prediction algorithm as given in section A.1. Moments of Characteristic Function 2, each pixel grayscale value in the original test image is Step 1 convert true color (RGB) image to a grayscale predicted by using its neighboring pixels’ grayscale image values. Step 2: Obtain the histogram of the grayscale image. Step2: Obtain a prediction-error image by subtracting the Step 3: Calculate the probability mass function and predicted image from the test image. characteristic function.. Step 3: Perform 3-level wavelet decomposition of the predicted image. A.2. Wavelet decomposition Step 4: Calculate the first three moments from the Step 1: Perform single-level discrete 2-D wavelet coefficients of the of the prediction-error image. transform on the image. Step 2: Obtain the first three moments from all the Original Image 200 Histogram of Original Image approximation, vertical, horizontal and diagonal co- 150 efficient. 100 50 Step 3: After obtaining all the above coefficients, 0 reconstruct the image is reconstructed using single-level 0 100 200 Predicted Image Histogram of Predicted Image inverse discrete 2-D wavelet transform. 1000 500 0 0 100 200 Figure 6. Comparison of histograms of original and predicted images 11 © 2010 ACEEE DOI: 01.IJSIP.01.03.5  
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  ACEEE Int. J.on Signal & Image Processing, Vol. 01, No. 03, Dec 2010   variance, skewness and kurtosis – are computed based on 2-D array of phase congruency of the reconstructed A.4. Phase Congruency image. Step 1: Calculate phase congruency of the images. Step 7: The same procedure is conducted for each Step2: Extract seven image features from the measure of reconstructed image Î0i (i=1, 2, 3) from the prediction- phase congruency – first three mean,variance and error image to collect another group of seven features. skewness. Original Image Phase Congruency B. Forming of feature database Step 1: Store all the 120 variables collected from each image as a row vector with 120 elements. Step 2: Collect all 120 variables from all 1845 images (933 authentic + 912 spliced). Predicted Image Phase Congruency Step 3: Save the entire variable set as a 1845 x 120 matrix to facilitate easy computation later. C. Image Classification Step 1: A database of all parameters are collected from all the images Step 2: Inputs are accepted from user: Figure 7. Extraction of phase congruency of original and predicted images Step 3: The user is asked to select any random image to be sent to the neural network A.5. Edge Detection Step 4: The given image is classified into different Step 1: Extract the edges of the image. subcategories Original Image Predicted Image Phase Congruency Step 5: The output is displayed. D. Mode of Classification • Classification of the images is done using 4 perceptrons. Edges in Original Image Edges in Predicted Image Edges in Phase Congruency • There are totally 1845 images in the dataset, with 120 different features being extracted from each. • Hence each perceptron receives 120 inputs, from 1845 images. Figure 8. Extraction of edges of original, predicted and phase congruency images IV. RESULTS AND DISCUSSIONS A.6. Final Feature Extraction Detection Rates Step 1: First 78 dimensional features are collected from The average detection rate of the experiments is the test image I and its prediction-error image Î. Step 2: shown in Table 2, where TP (true positive) represents the 3-D Daubechies wavelet decomposition is performed on detection rate of spliced images, TN (true negative) the test image. represents the detection rate of authentic images, and Step 3: For each wavelet subband, the first three accuracy is the average detection rate. moments are derived according to Equation (2), resulting in 39 dimensional features. Step 4: Similarly, another 39 dimensional features are extracted from prediction-error image. Step 5: The additional 42 dimensional features are collected from three reconstructed images generated from the test image and three reconstructed images generated from the prediction-error image. Step 6: From each reconstructed image I0i (i = 1, 2, 3), seven image features: first three moments are calculated according to Equation (2) , and four statistics – mean, 12 © 2010 ACEEE DOI: 01.IJSIP.01.03.5  
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  ACEEE Int. J.on Signal & Image Processing, Vol. 01, No. 03, Dec 2010   TABLE 2 - Detection rates using a Perceptron-based classifier [2] Zhen Zhang, Yukun Bian, Xijian Ping, “Image Blind Training Size 9/10 Forensics Using Artificial Neural Network”, International Conference on Computer Science and TP TN Accuracy Software Engineering, 2008. Perceptron 0.9605 0.9475 0.9540 [3] Yun Q. Shi, Chunhua Chen, Wen Chen, “A Natural Training Size 5/6 Image Model Approach to Splicing Detection”, Proceedings of the 9th workshop on Multimedia & TP TN Accuracy Security, Dallas, Texas, USA, 2007. Perceptron 0.9178 0.9164 0.9171 [4] Data set of authentic and spliced image blocks, Training Size ½ DVMM, Columbia Univ., http://www.ee.columbia.edu/dvmm/researchProjects/ TP TN Accuracy AuthenticationWatermarking/BlindImageVideoForen Perceptron 0.7785 0.7513 0.7649 sic/ Training Size 1/3 [5] H. Farid, “A Picture Tells a Thousand Lies”, New Scientist, 179(2411), pp. 38-41, Sept. 6, 2003. TP TN Accuracy [6] T.-T. Ng, S.-F. Chang and Q. Sun, “Blind Detection 0.6590 0.6624 0.6607 of Photomontage Using Higher Order Statistics”, IEEE ISCAS, May 2004. V. CONCLUSION [7] M. K Johnson and H. Farid, “Exposing digital forgeries by detecting inconsistencies in lighting”, In this paper, a new modified splicing detection ACM Multimedia and Security Workshop, 2005. scheme is proposed. To detect the spliced images, the [8] Y.-F. Hsu and S.-F. Chang, “Detecting image distinguishing image features are extracted by exploiting splicing using geometry invariants and camera both magnitude and phase information of a given image. characteristics consistency”, IEEE ICME, July 2006. The first part of image features is the statistical moments [9] Y.Q. Shi, G. Xuan, D. Zou, J. Gao, C. Yang, Z. of characteristic functions of a test image, its prediction- Zhang, P. Chai, W. Chen and C.H. Chen, error image, and their wavelet subbands.The methodology implemented in this paper reduces the “Steganalysis based on moments of characteristic computation time and maintains good accuracy. functions using wavelet decomposition, prediction- error image and neural network”, IEEE ICME, July REFERENCES 2005. [10] A. V. Oppenheim and J. S. Lim, “The importance of [1] Wen Chen, Yun Q. Shi, Wei Su, “Image splicing detection phase in signals”, Proc. Of the IEEE, vol. 69, pp. using 2-D phase congruency and statistical moments of 529-541, May 1981. characteristic function ”, Proceedings of SPIE, San Jose, CA, [11] M.C. Morrone and R.A. Owens, “Feature detection USA, 2007. from local energy”, Pattern Recognition Letters,vol.6,pp.303-313,2000.. 13 © 2010 ACEEE DOI: 01.IJSIP.01.03.5