Simulative Comparison of Copy- Move Forgery Detection Methods for Digital Images

Transcription

1 Simulative Comparison of Copy- Move Forgery Detection Methods for Digital Images Harpreet Kaur 1, Jyoti Saxena 2 and Sukhjinder Singh 3 1 Research Scholar, 2 Professor and 3 Assistant Professor 1,2,3 Department of Electronics & Communication Engineering 1,2,3 Giani Zail Singh Punjab Technical University Campus, Bathinda , Punjab (India) Abstract Copy-move image forgery is a simple and effective operation in which a region of an image is copied and then pasted to another location in the same image after applying post operations like scaling, rotation, compression or noise addition etc. The aim behind this type of forgery may be to hide some particularly important details in the image. The problem of detecting, whether an image has been forged or not; has been resolved by implementing a block based method- PCA combined with a key-point based method-sift (known as PCA+SIFT method) using MATLAB platform. Moreover, the performance of above said method has been compared with SURF and DWT+SIFT methods in terms of sensitivity, specificity, accuracy, FPR and FNR using MICC-220 dataset. It has been observed that PCA+SIFT method shows effective detection on MICC-220 dataset with duplicated, noisy and compressed regions. Experiments results also prove the effectiveness of PCA+SIFT method in dealing with multiple cloning. Key-words Copy-move image forgery, key-point based method, block based method, SURF, PCA and SIFT. 1. INTRODUCTION Due to advancement and availability of powerful image processing softwares such as Adobe Photoshop and corel paint shop; it is easy to do manipulation in digital images without leaving any remarkable traces. The manipulation of the digital image to conceal some meaningful information or to create misleading image is called an image forgery [5]. Addition or deletion of some content of the image is a popular form of creating forgery in images. Image forgery can be broadly classified in three categories namely image forgery using splicing, copy-move image forgery and image re-sampling [10]. In this paper, detection of copy-image forgery in digital images has been carried out by implementing copy-image forgery detection methods namely SURF (Speed Up Robust Features) and PCA+SIFT (Principal Component Analysis + Scale Invariant Feature Transform) talking MICC-220 dataset using MATLAB platform. Further, the comparative analysis of above said methods with DWT+SIFT (Discrete Wavelet Transform + Scale Invariant Feature Transform) method has been carried out in terms of sensitivity, specificity, accuracy, FPR (False Positive Rate) and FNR (False Negative Rate). The PCA+SIFT method has also been tested against compression and noise attacks. The rest of the paper is structured as follows: section-2 presents copy-move image forgery and its detection methods. The contribution of various researchers to detect copy-move image forgery is discussed in section-3. Section-4 presents methodology for copy-move image detection methods, while experimental results and discussion on forgery detection are presented in Section-5. Conclusions are finally drawn in Section COPY-MOVE IMAGE FORGERY DETECTION Copy-move is an image forgery method in which parts of an original image (after some possible geometric and illumination adjustments) are copied, moved to a desired location in the image and then pasted (e.g. refer figure 1). The main aim of copy-move image forgery is to hide certain details or to duplicate some aspects of the image [7]. (a) Original Image (b) Forged image Figure 1: Example of copy-move image forgery As the copied part belongs to the image itself, its properties such as texture and RGB values remain the same. This correlation makes detection of copy-move image forgery difficult [6]. In literature generally block and key-point based methods are used to detect copy-move forgery in digital images as depicted in figure 2. Figure 2: Copy-move image forgery detection methods These methods have been briefly discussed in the following subsections. 2.1 Block Based Methods In block based methods, an image is segmented into rectangular or circular overlapping small blocks; feature vectors are extracted and then these are lexicographically 62

2 sorted to check whether adjacent blocks are similar or not. Finally, matching similar blocks leads to forgery detection. The most commonly used block based methods are DCT (Discrete Cosine Transform), PCA (Principal Component Analysis), DWT (Discrete Wavelet Transform), DyWT (Dyadic Wavelet Transform), FMT (Fourier Mellin Transform), Zernike etc. Block based methods can generally deal with post operations like compression or noise in the copied part but cannot handle geometrically transformations like high degree rotation and scaling. Moreover, these methods are found to be computationally inefficient, hence take more time [11]. 2.2 Key-point Based Methods Key-point based methods compute the feature vectors for regions with high entropy in an image and compare them for forgery detection. SIFT (Scale in-variant Feature Transform), SURF (Speed Up Robust Features), ORB (Oriented FAST and Rotated BRIEF) are the examples of key-point based methods [9]. Key-point based methods can easily tackle the geometrical transformations but cannot deal with compression or noise addition in the cloned region efficiently. However these methods are faster than block based methods due to their computational efficiency. To combine the advantages of both methods, a hybrid method has been implemented by combing block and key-point based methods in this paper. Experiments results are presented to prove that the hybrid method is able to precisely individuate the tampered image and quantify its robustness and sensitivity to image post-processing. 3. RELATED WORK Amerini et al. proposed a new methodology based on SIFT that was able to detect geometric transformations of the cloned part like rotation and scaling [1]. This method also dealt with multiple cloning successfully. The use of clustering algorithm reduced the False Positive Rate of the detection result. Gharibi et al. presented an efficient block based method to detect copy-move image forgery i.e. Principal Component Analysis (PCA) for dimension reduction of feature vectors [8]. In this method, the image was divided into overlapping blocks and Gabor filter was used to extract the feature vector of each block. Experimental results proved the effectiveness of this method for accurate detection of copied regions even when these regions had undergone lossy compression. Mohammad Farukh Hashmi et al. proposed a copy-move forgery detection method using DWT and SIFT features. DWT was used for reducing dimension which in turn increased the accuracy of results and robustness was introduced by SIFT [7]. The proposed method was tested with MICC-F220 dataset and was able to handle geometric transformations like rotation and scaling in the copied patch. The proposed method reduced computational complexity, decreased computational time and increased accuracy. 4. METHODOLOGY In this paper, the detection of copy-move image forgery has been studied and implemented by two methods namely SURF and PCA+SIFT by taking a common dataset MICC-F220 using MATLAB platform. The results of the above said 63 methods have been compared with DWT+SIFT method [7] on the basis of various evaluation metrics such as sensitivity, specificity, accuracy, FPR and FNR and are briefly represented in the following Table 1. Table 1: Various evaluation metrics used for this work The algorithms to detect copy-move image forgery using the above said methods are briefly described in the following subsections. 4.1 Copy-Move Forgery Detection using SURF To achieve very fast speed in copy-move image forgery detection, SURF features are used to find the image key-points (interest points) and extract their 64-dimensional descriptor. These SURF features are then matched to detect the forged region. The algorithm for copy move forgery detection in digital images using SURF is briefly outlined in the following steps [2]: Step 1: Read an image. Step 2: Identify image key-points / interest points using SURF key-point detector. Step 3: Compute the descriptors for the key-points using SURF key-point descriptor. Step 4: Identify the best ten matches for every key-point. Step 5: Match the extracted features using the concept of Euclidian distance. Step 6: Step 7: Perform the dynamic thresholding. Join the matched key-point using a line to represent the copied region. Above mentioned steps have been applied taking MICC-F220 dataset using MATLAB platform and results are presented in the Section Copy-Move Forgery Detection using PCA+SIFT A hybrid approach known as PCA+SIFT method has also been implemented using MATLAB platform taking MICC-F220 dataset. The steps for the same have been discussed below [1, 3 and 4]: Step 1: Dimension Reduction using PCA: Firstly, test image is converted into gray scale image and its dimensions are reduced using PCA. PCA returns the principal component coefficients (also known as loadings) of a matrix (say X). Rows and columns of X correspond to observations and variables respectively. Each column of this matrix represents coefficients for one principal component. Number of Principal Component columns (PCs) is taken 230

3 for this work. Step 2: Key-point Detection and Feature Extraction: A set of n key-points with their descriptors are extracted using SIFT from the image obtained in step 1. SIFT descriptor is basically a feature vector containing 128 elements. Each element of these feature vectors is invariant to any scaling and rotation of the image. Step 3: Multiple Key-point Matching using g2nn: The keypoints extracted by SIFT are then matched based on their feature vectors using g2nn algorithm. The g2nn (generalization 2 Nearest Neighbor) algorithm is used for detecting the match between two key-points. For checking the key-point match; the ratio of Euclidian distance of the closest neighbor to that of second closest one is calculated and it is compared with threshold (T). The key-point is said to be matched only if the ratio is less than T. The value of T may lie between 0 and 1. In this paper threshold has been set to value 0.5. Step 4: Agglomerative Hierarchical Clustering and Forgery Detection: The matched key-points are clustered using Agglomerative hierarchical clustering. This clustering helps to reduce the false matches of the key-points. An Image is said to be forged by copy-move forgery when two or more than two clusters linked to each other through at least three matching key-points. These are represented by matching lines of red color. Step 5: Transformations Estimation: To estimate transformations like scaling and rotation as applied between original region and copy-moved version an estimation algorithm known as RANSAC (RANdom Sample Consensus) is being used here. The inliers and outliers are chosen on the basis of a specific distance denoted as β (which is taken here as 0.05). Now the estimated transformations associated with the higher number of inliers is chosen after a predefined number of iterations R iter (which is taken here as 1000). Following table (refer Table 2) summarizes various parameters taken for this experiment work. Table 2: Various parameters taken for this work Threshold in key-point matching Maximum distance of inliers in RANSAC Number of RANSAC iterations Number of Principal Component columns The initial processing of image by PCA makes the estimation of translations like added noise, compression effective. 5. RESULTS AND DISCUSSION In this section, results have been presented to verify the performance and effectiveness of copy-move image forgery detection methods namely SURF and PCA+SIFT taking a common MICC-F220 dataset (consisting of 220 images: 110 are tempered images and 110 are original images) on MATLAB R2013a programming tool on a PC with Windows 8.1 and i5 processor. A comparative study has 64 been carried out to compare these methods with other method in literature such as DWT+SIFT Results of SURF Descriptor To demonstrate the performance of the SURF descriptors; detection results of four images are presented here. Experimental results of a copy-move image forgery detection based on the SURF (Speed up Robust Features) descriptors are depicted in the figure 3. Figure 3: The original images are shown in the first column; the tempered images (including rotation, scaling, both rotation and scaling in cloned region) is pictured in the second column and the detection results using SURF descriptors are reported in the third column respectively Results show that the SURF descriptors are invariant to geometrical transformations such as rotation, scaling, both rotation and scaling etc. However, it has been also observed that it is not quite suited in detecting the image region duplication consisting of additive noise and blurring. Table 3 gives calculation of various parameters such as TP, TN, FP and FN for SURF descriptors. Table 3: TP, TN, FP and FN for SURF Descriptors Number of authentic images Number of forged images TP TN FP FN Results of PCA Combined with SIFT Experimental results to verify the behavior of the PCA+SIFT are presented here; detection performances and geometric transformation parameters estimation are investigated as well. Furthermore, tests to check the robustness of the method against usual attacks such as compression or noise addition, the copied region may undergo, have also been carried out. Five random original images from the same dataset are chosen and forged by copying and pasting (after applying transformations such as compression and noise) a small rectangular image block over another in the same image. The noise and compression added to the copied patch are zero mean white Gaussian noise with variance and two dimensional true compression with 4.90% Compression Ratio (CR) and 1.19 Bit Per-Pixel ratio (BPP) respectively. These images have been used to analyze the performance of SIFT combined with PCA against transformation such as noise and

4 compression. Figure 4 shows that the SIFT + PCA method can accurately detect and locate forgery region and has resistance ability for post processing such as rotation, scaling, both rotation and scaling, added Gaussian noise, compression etc. Further a comparative study presented in figure 5 shows that SIFT+PCA method achieves higher sensitivity, accuracy and has lesser FNR as compared with SURF and SIFT+DWT methods. However SIFT+DWT is better as compared with SURF and SIFT+PCA in terms of specificity and FPR. Figure 4: The original images are shown in the first column; the tempered images (including rotation, scaling, both rotation and scaling, Gaussian noise, compression in cloned region) is pictured in the second column and the detection results using PCA+SIFT are reported in the third column respectively Table 4 gives calculation of various quantitative parameters such as TP, TN, FP and FN for copy-move forgery detection using SIFT + PCA on common MICC-F220 dataset. Table 4: TP, TN, FP and FN for SIFT + PCA Number of Number of TP TN FP FN authentic images 65 forged images Qualitative Comparison of SURF, DWT+SIFT and PCA+SIFT The performance of an image-forgery detection system can be measured in terms of sensitivity, specificity, accuracy, FPR and FNR. Table 5 represents above said performance metrics for SURF, SIFT+DWT [7] and SIFT+PCA to carry out qualitative analysis. These performance metrics have been calculated for above said methods by using the definitions as given in the Table 1 and values of Table 3, Mohammad Farukh Hashmi et al. [7] and Table 4 respectively. Table 5: Results of SURF, SIFT+DWT and SIFT+PCA Evaluation Methods Metrics SURF DWT+ SIFT [7] PCA +SIFT Sensitivity (%) Specificity (%) Accuracy (%) FPR (%) FNR (%) Figure 5: Comparison of SURF, SIFT+DWT [7] and SIFT+PCA 6. CONCLUSION AND FUTURE WORK In the digital era, detection of copy-move image forgery has become a challenging issue. Different copy-move image forgery detection methods are available in the literature. In this paper copy-move image forgery detection methods such as SURF and PCA combined with SIFT (PCA+SIFT), taking a common MICC-F220 dataset have been implemented using MATLAB platform. A comparative study of above said methods has been carried out with DWT combined with SIFT (DWT+SIFT) in terms of sensitivity, specificity, accuracy, FPR (False Positive Rate) and FNR (False Negative Rate). Experiment results show that PCA+SIFT method achieves higher values of sensitivity, accuracy and lower value of FNR as compared with SURF and DWT+SIFT methods even if the copied part is rotated, scaled, both rotated and scaled. Moreover it gives reasonable values of specificity and FPR. Robustness of the same was checked by testing tampered images where copied part was attacked with Gaussian noise and compression and then pasted. It has also been observed that the detection accuracy of PCA combined with SIFT method is superior to SURF and DWT combined with SIFT methods for above said cases. However this method is unable to detect image forgery in flat region considerably. Thus future work may be extended to resolve this issue. REFERENCES [1] Xunyu Pan and Siwei Lyu, (2010), Region Duplication Detection Using Image Feature Matching, IEEE Transactions on Information Forensics and Security, 5(4), pp [2] Xu Bo, Wang Junwen, Liu Guangjie and Dai Yuewei, (2010), Image Copy-Move Forgery Detection Based on SURF, International Conference on Multimedia Information Networking and Security, pp [3] Gharibi F., Jamjah J. R., Akhlaghian F., Azami B. Z. and Alirezaie J., (2011), Robust Detection of Copy- Move Forgery using Texture Features, IEEE 19 th Conference on Electrical Engineering (ICEE), pp.1-4 [4] Amerini Irene, Ballan Lamberto, Caldelli, Roberto

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