HOUGH TRANSFORM GENERATED STRONG IMAGE HASHING SCHEME FOR COPY DETECTION

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1 How to cite tis article: Srivastava, M. Siddiqui, J., & Ali, M. A. (218). Houg transform generated strong image asing sceme for copy detection. Journal of Information and Communication Tecnology, 17(4), HOUGH TRANSFORM GENERATED STRONG IMAGE HASHING SCHEME FOR COPY DETECTION 1 Mayank Srivastava, 2 Jamsed Siddiqui & 3 Moammad Atar Ali 1 Institute of Engineering and Tecnology, Ganesi Lal Agrawal University, India 2 Department of Computer Science, Aligar Muslim University, India 3 Department of Applied Computing, University of Buckingam, United Kingdom mayank.srivastava@gla.ac.in; jamsed_faiza@rediffmail.com; atar.ali@ buckingam.ac.uk ABSTRACT Te rapid development of image editing software as resulted in widespread unautorized duplication of original images. Tis as given rise to te need to develop robust image asing tecnique wic can easily identify duplicate copies of te original images apart from differentiating it from different images. In tis paper, we ave proposed an image asing tecnique based on discrete wavelet transform and Houg transform, wic is robust to large number of image processing attacks including sifting and searing. Te input image is initially pre-processed to remove any kind of minor effects. Discrete wavelet transform is ten applied to te pre-processed image to produce different wavelet coefficients from wic different edges are detected by using a canny edge detector. Houg transform is finally applied to te edge-detected image to generate an image as wic is used for image identification. Different experiments were conducted to sow tat te proposed asing tecnique as better robustness and discrimination performance as compared to te state-of-teart tecniques. Normalized average mean value difference is also calculated to sow te performance of te proposed tecnique Received: 7 April 218 Accepted: 3 August 218 Publised: 1 October

2 towards various image processing attacks. Te proposed copy detection sceme can perform copy detection over large databases and can be considered to be a prototype for developing online real-time copy detection system. Keywords: Content-based copy detection, digital watermarking, discrete wavelet transform, oug transform, image forensics, image asing. INTRODUCTION Te use of digital media is increasing in our day to day life due to te adaptability of a large number of smart devices like smartpones. Tese devices allow us to do a lot of application-oriented tasks very easily, including capturing and editing of images. We know it very well tat te use of editing software does not require any special tecnical expertise and tey can easily manipulate images (Liu, Wang, Lian, & Wang, 211). Massive creation and widespread dispersion of data, arising from easy to copy nature, poses new callenges for te protection of intellectual property of multimedia data (Kang & Wei, 29). Protecting te copyrigt of an image is a matter of great concern (Qazi, Hayat, Kan, Madani, Kan, Kolodziej, Lin & Wu, 213). To ensure tat te given image is original and is not a modified version, image autentication tecniques are required (Battiato, Farinella, Messina, & Puglisi, 212). Traditionally, autentication issues are addressed by cryptograpic ases, wic are sensitive to eac bit of te input message. As a result, cange in even a single bit of te input data leads to a significant cange in te as value (Quresi & Derice, 215). However, due to te ig sensitivity of te input data tese as functions are not suitable for image autentication. In tis context, we need to explore te area of image forensics (Redi, Tatak, & Dugelay, 211) wic involves a combination of tecniques used not only to verify te autenticity of an image but also to verify ownersip and detect unautorized copies. Currently, two approaces named as watermarking and content-based copy detection are used to detect unautorized copies. In watermarking, an autenticator is generated and added to te media content wic is used to identify te autenticity of an original content (Rey & Dugelay, 22). Content-based copy detection (CBCD) is an alternative to digital watermarking, in wic multimedia content itself is used to establis its ownersip. (Hsiao, Cen, Cien, & Cen, 27). In image-based copy detection, unique features are extracted from an image wic can be used for identification. Over te last few years, a number of significant works ave been proposed in te area of image asing wic is an extension of te content 654

3 based copy detection tecniques (Tang, Yang, Huang, & Zang, 214b). In image asing, te generated unique feature is represented as small, preferably bit level data to form te image as, wic is used for image identification (Tang, Zang, Dai, Yang, & Wu, 213a). Ideally, image asing sould be able to discriminate between similar and dissimilar images, i.e. te mecanism sould depict robustness and discrimination among te images. Apart from tat, it sould be robust to various kinds of image processing attacks besides fulfilling te properties related to specific applications. LITERATURE REVIEW In te past, researcers ave implemented many algoritms related to various aspects of image asing. Some of te notable algoritms categorized on te basis of transformation/functionalities used are as follows: Tang, Yang, Huang, and Zang (214b) proposed image asing based on dominant discrete cosine transform (DCT) coefficients wic ave been proven to perform well in classification and in detecting image copies. Tang, Wang, and Zang (21) used a mecanism based on a dictionary, wic represents te caracteristics of various image blocks. Te proposed mecanism depicted a low collision probability. DCT-based tecniques fail against geometric transformations (Al-Qersi & Ko, 213). Lei, Wang, and Huang (211) proposed a novel robust asing metod based on te use of radon transform and discrete Fourier transform (DFT). Te algoritm performs well in detecting copies wit a small as size. Wu, Zou, and Niu (29) proposed a asing algoritm based on radon and wavelet transform, wic can identify content canges. Unfortunately, radon transform is not resistant to all te geometric transformations suc as sifting & searing. (Wu, Zou, & Niu, 29). Amed, Siyal, and Abbas (21) proposed a robust as-based sceme, were pixels of eac block are randomly modulated to produce te as. Suc algoritms ave proven to exibit good time-frequency localization property. Kars, Laskar, and Aditi (217) proposed an image asing, were four- level 2D-DWT is applied along wit SVD to produce te image as. Cen and Hsie (215) proposed an algoritm, were 128-dimensional SIFT features are extracted from a normalized image. Te proposed sceme significantly reduces te retrieval time wit a minor loss of accuracy. Ling, Yan, Zou, Liu, and Feng (213) proposed a fast image copy detection approac based on local fingerprint defined visual words. Te mecanism outperforms similar state-of-te-art metods in terms of precision and efficiency. Lv and Wang (212) proposed a tecnique similar to Ling et al. (213), werein te Harris detector is used to select te most stable key-points wic are less vulnerable to image processing attacks after te application of SIFT. 655

4 Tang, Dai, Zang, Huang, and Yang (214) proposed a block-based robust image asing based on color-vector angles and discrete wavelet transform (DWT). Te proposed mecanism is robust to normal digital operations including rotation up to 5 o. Tang, Huang, Dai, and Yang (212b) proposed te use of multiple istograms in wic normalized image is divided into different rings wit equal area and ten ring-based istogram features are extracted. Te proposed mecanism is claimed to be resilient against rotation of any arbitrary angle. Tang, Zang, Huang, and Dai (213) proposed anoter asing on te basis of ring-based entropies wic outperforms similar tecniques in terms of time complexity. Tang, Zang, Li and Zang (216) proposed a robust image asing metod based on ring partition and four statistical features, i.e. mean, variance, skewness and kurtosis. Tang, Zang, and Zang (214) proposed image asing based on ring partition and nonnegative matrix factorization (NMF). Here, NMF is applied to te secondary image produced on te basis of ring partition. Te algoritms sow good robustness against rotation and ave good discriminative capability. Tang, Wang, Zang, Wei, and Su (28) proposed a robust asing in wic NMF is applied to produce a coefficient matrix, wic is coarsely quantized to produce te final as. Te algoritm exibits a low collision probability. Kars, Laskar, and Ricariya (216) proposed image asing on te basis of ring-based projected gradient non-negative matrix factorization (PG-NMF) features and local features. PGNMF generated features are combined wit salient region-based features to produce te final as. Te metod is robust to content preserving operations and is capable of localizing te counterfeit area. Tang, Dai, and Zang (212a) proposed perceptual asing for color images using seven invariant moments. Tese moments are invariant to translation, scaling and rotation and ave been widely used in image classification and image matcing. Altoug many asing algoritms ave been reported, tere are still some practical problems in asing design. More efforts are needed for developing ig-performance algoritms aving a desirable balance between robustness and discrimination particularly considering sifting and searing attacks. Few of te algoritms ave claimed varying degrees of success against searing (Lei, Wang, & Huang, 211; Zou et al., 213; Lv & Wang, 212). However, for sifting only one autor reported its use (Wu, Zou, & Niu, 29). In tis work, we ave proposed an image asing based on a Houg transform and DWT wic is robust to sifting & searing apart from giving comparable performance against different image processing attacks. Te key advantage of using Houg transform is tat it is tolerant of gaps in te edges and 656

5 relatively unaffected by te noise & occlusion in te image. DWT can be used to convert a signal to its approximation based sort representation. As sifting & searing attacks cange te orientation of te image, keeping rest of te image contents uncanged, Houg transform is applied to get its unique edge based feature for better identification. Many experiments ave been conducted to validate te efficacy of our tecnique. receiver operating caracteristics (ROC) curve comparisons wit some of te representative asing algoritms are also done, and te results indicate tat proposed asing outperforms te compared algoritms in terms of classification performance. Te rest of te paper is arranged as follows: Te next section describes te proposed image asing followed wit te section tat gives experimental results. PROPOSED IMAGE HASHING In tis section, we analyze te basic properties of te DWT followed wit a brief description of canny edge detector. Houg transform, wic is used to generate te image as on te basis of canny edge detection is ten explained in detail. Finally, te proposed approac is given wic is based on te features given above. Discrete Wavelet Transformation In image processing, 2D wavelet is of great importance were te transformation is first applied along te rows of te image followed by transformation along te columns of te image. Suc a process generates four sub-band regions LL, LH, HL and HH were LL represents blur and LH, HL & HH represents orizontal, vertical and diagonal differences respectively (Lu & Hsu, 25; Tanki, Dwivedi, & Borisagar, 217). DWT decomposes a signal into a set of mutually ortogonal wavelet basis functions and it is invertible, wic means tat te original signal can be completely recovered from its DWT representation. Te main advantage of using wavelet transformation is its efficiency in converting a signal to its sort representation (Tang, Dai, Zang, Huang, & Yang, 214a). Let A is N x N matrix; W N is a wavelet transformation matrix; W N T is te transposed values of W N. Te product A*W N T processes te rows of A into weigted averages and differences. Similarly, te product W N *A simply transforms te column of A into weigted averages and differences. Tus, two-dimensional DWT can be easily represented as W N AW NT. In our implementation, we ave used Daubecies wavelet transform were four-term ortogonal filter is constructed by using low-pass filter =(, 1, 2, 3 ) and te 657

6 658 ig-pass filter g=(g,g 1,g 2,g 3 ). Matematically, suc a wavelet transform built from given and g tat is applied to vectors of lengt N=8 can be written in block format as follows: (1) Next, we compute W 8 W 8 T and sow tat if W 8 ortogonal ten it gives: (2) were I 4 is te 4x4 identity matrix and 4 is te 4x4 zero matrix. After computation we get te following value for I 4 : (3) were a = and b = In tis way, te following nonlinear equations are generated and are used to produce te Daubecies filter components. (4) (5) (6) (7) Te above generated filter components are used to produce te different sub-bands of te input image. In our proposed algoritm, we only use te approximation values (LL) of te transformed image for furter steps of as generation. Houg Transform Houg transform is used to identify specific sapes in an image. It converts all te points in te curve to a single location in anoter parameter space by G H W I I GG GH HG HH G H G H W W T T T T T T T a b b b a b b a b b b a I

7 coordinate transformation (Fig. 1). Houg transform is applied to te image tat is obtained after applying one of te edge detection algoritms like canny edge detection, wic returns a binary image containing 1 s were it finds edges in te input image and s elsewere (Si, 21). Figure 1. Parametric description of a straigt line. Te Houg transform is used to detect straigt lines uses te following parametric representation of te line (Aminuddin et al., 217). r x* cos( teta ) y *sin( teta ) (8) Here r is te distance from te origin to te line, along a vector perpendicular to te line and teta is te angle between te x-axis and te line (Si, 21). Te calculation of te Houg transform is a parameter space matrix wose rows and columns correspond to te values of r and teta, respectively. For every point of interest in te image, r is calculated for every teta and it is rounded off to te nearest value. Te value of tat accumulator cell is incremented by one. At te end of tis procedure, any value T in te matrix means tat T points in te XY plane lie on te line specified by distance r and angle teta. Peak values in te matrix represent te potential lines in te input image. Te algoritm for Houg transform can be given as follows: 1. Identify te maximum and minimum values of r and teta. 2. Subdivide te parametric space into accumulator cells. 3. Initialize te accumulator cells to be all zeros. 4. For all edge points (x,y) in te image a. Use gradient direction for teta. b. Compute r from te equation. c. Increment A(r, teta) by one. 659

8 5. In te end, any value Q in A(r,teta) means Q points in te XY plane lie on te line specified by angle teta and r. 6. Peak values in te accumulator matrix A(r,teta) represents potential lines in te input image. Houg transform maps eac of te points in te input image into sinusoids. As given above, te Houg transform is tolerant of gaps in te edges and terefore it is relatively unaffected by te noise in te image. Implementation Approac Te as generation algoritm consists of various steps of preprocessing, transformation and as generation. Te process for feature extraction is sown in Fig. 2. Figure 2 Basic block diagram of te proposed asing. Preprocessing Te first step is normalization in wic te input image is normalized by employing image resizing and color space conversion. Image resizing is used to resize te original image to a standard size of Te image tus produced is converted to a grayscale image for furter processing and as generation. Transformation and Has Generation In te next step, te processed image is filtered troug 2D DWT by using Daubecies wavelet filter. After applying te wavelet transform, te four different sub-bands are generated were we use approximation coefficients of size 256x256 for furter processing. Te Canny edge detection is ten 66

9 applied to te approximation matrix to produce a binary image (BW). It is important ere to specify tat BW is logical and aving a size of 256x256. Houg transform is ten applied to te generated BW matrix to produce a matrix of size 1445x36, were te rows correspond to te distance bins and te columns correspond to te angle in teta. Row-wise mean is calculated to produce a column vector of size 1445x1. Suc integer column vector is used as an image as for image identification. Similarity Metric To measure te similarity between a pair of ases, L1 norm is used, wic is one of te standard metods used for measuring te as distance (Lei, Wang, & Huang, 211). Let 1 and 2 be two image ases, ten as distance can be calculated as follows: Has distance n HD 1 i 2 i i1 (9) If te as distance (HD) is less tan a predefined tresold T, te images are considered to be visually identical. Oterwise, tey are classified as different images. EXPERIMENTAL RESULTS To demonstrate te efficacy of te proposed mecanism, we conduct a series of experiments to verify te proposed approac s accuracy, efficiency and sensitivity against a number of image processing attacks. Robustness Analysis Te proposed tecnique is applied to test images from te USC-SIPI image database (USC-SIPI, 27). A sample of some of te standard images is sown in Fig. 3. Eac of te original images is used to create 88 modified versions by employing a number of image processing operations suc as rescaling, brigtness adjustment, contrast adjustment, gamma correction, Gaussian low-pass filtering, rotation as tese are used in most of te researc papers of te area of image asing (Tang et al., 214b), (Tang et al., 213a), (Tang et al., 28), (Tang et al., 214c). Te modified versions were created using MATLAB wit te attack parameters as sown in Table 1. For example, 661

10 we take an input image like Airplane and create its brigtness adjustment based attacked copies by canging its intensity values as mentioned in Table 1. Similarly, duplicate copies based on different attacks of te original image Airplane is created by using attacks given in Table 1. Tis process of duplicate image creation will be over, only wen we create te duplicate copies of all te original images wic are to be used in te experiment. After generating te duplicates, ases are extracted from all te images including te original one and te Has distance is calculated between te original image and its duplicate copies. Definitely, te as distance in suc a scenario represents te distance between as of eac of te original image and its different attacked copies. Te as distance value, wic is categorized on te basis of different attacks for all te considered images, is given in Table 2. Figure 3. Standard bencmark images used. Table 1 Generation of Duplicate Copies of Original Images Attack Details Parameter variation No. of images Brigtness adjustment Intensity values.5,.1,.15,.2 4 Contrast adjustment Intensity values.75,.8,.85,.9 4 Cropping Xmin, Ymin (2,2), (4,4), (6,6), (9,9), (11,11) 5 (continued) 662

11 Attack Details Parameter variation No. of images Gamma correction Gamma 1.25, 1.5, 1.75, x3 Gaussian low-pass filtering Standard deviation.3,.4,.5,.6,.7,.8,.9, 1. 8 Gaussian Noise Variance.1,.2,.3,.4,.5 5 JPEG Compression Quality 3, 4, 5, 6, 7, 8, 9, 1 8 Median Filter Neigborood (3,3), (5,5), (7,7), (9,9), (11,11) 5 Rescaling Ratio.5,.75,.9, 1.1, 1.5, 1.75, 2. 7 Rotation Angle -1, -.75, -.5, -.25,.25,.5,.75, 1 Salt & Pepper Noise density.1,.3,.5,.7,.9 5 Speckle Variance.2,.4,.6,.8, 1. 5 Sift-H Positions 1, 2, 3, 4, 5 5 Sift-V Positions 1, 2, 3, 4, 5 5 Sift-HV Positions (1 1), (2 2), (3 3), (4 4), (5 5) Searing Transformation values.1,.2,.3,.4,.5 5 Total Table 2 presents te maximum, minimum and mean of as distance under different attacks. It is observed tat all te mean values are less tan.7, wile te maximum distance, taking into account all attacks, is less tan 2.6. It is justifiable to coose a distance value of.78 as te tresold on wic te proposed tecnique is resistant to most image processing operations. In tis case, 96.59% visually similar images are correctly identified as copies of original images. In tis experiment, we ave used 1 original images and created 88 copies of eac of te original images to produce 88 copies. Out of te total number of 88 duplicate copies, our system can correctly identify 85 as copied ones, i.e % as copied ones. Ideally, we are looking for tat tresold value were te percentage of visually similar images identified as copied is iger and te percentage for different images identified as similar images is low. It is inferred tat te tresold value of.78 gives good experimental results. 663

12 Table 2 Maximum, Minimum, Mean and Standard Deviation of Has Distances for Different Attacks Attack Max Min Mean Std Dev Brigtness Adjustment Contrast Adjustment Cropping Gamma Correction Gaussian Low-pass filtering Gaussian Noise JPEG Median filter Rescaling Rotation Salt & Pepper Speckle Sift-H Sift-HV Searing Sift-V Discrimination Analysis To demonstrate discriminability, 36 different images of sizes ranging from to are collected from USC-SIPI database (USC-SIPI, 27). Te Has distance is calculated between eac pair of 36 images to generate 63 different as distances. Te distribution of suc Has distances is sown in Fig. 4. Te maximum, minimum, mean and standard deviation calculated on te basis of 63 as distances are 5.93,.417, 1.8 and.99 respectively. If te tresold is.78, ten 4.45% different images are falsely identified as similar images, wic is because out of 63 calculated as distances 28 is aving values less tan a tresold of.78. In general, a small tresold will improve te discrimination but simultaneously decreases robustness. Keeping in view tis important point, tresold must be cosen depending upon te requirements of te specific application. Te mean value of discrimination is 1.8, wic is more tan four times larger tan te igest mean of robustness except for Gaussian noise and median filter. For Gaussian noise and median filter, te mean of discrimination value is almost tree times larger tan te igest mean of robustness. Also, 664

13 te maximum value of discrimination is 5.93 wic is more tan six times larger tan te maximum value of robustness, except for Gaussian noise and salt & pepper. For Gaussian noise and salt & pepper, tis value is almost more tan twice te maximum value of robustness. Ideally, a copy detection tecnique sould exibit very low values corresponding to robustness and ig values corresponding to discrimination. Tis would imply tat te tecnique is capable of correctly identifying duplicated copies wile at te same time rejecting different images. Keeping in view tis definition of robustness and discrimination, te proposed asing exibits promising results as evidenced by te grap sown above. 1 8 Frequency Has distance Figure 4. Distribution of as distance for discrimination. Normalized Average Mean Value Difference Te observations based on te Has distance presented in te previous section were categorized on te basis of different attacks. In tis section, te analysis is performed image-wise and te maximum, minimum, mean and standard deviation of te Has distance is calculated by considering all but one of te attacks. Since, 16 different kinds of attacks ave been considered, suc an analysis results in 16 different maximum, minimum, mean and standard deviation values. Furter, a set of values is obtained wen all te attacks are considered togeter. Te difference between te mean values is obtained by considering all te attacks and all but one of te attacks. Finally, te averaging of te difference values is done in order to reac to some conclusion. One of te important reasons to consider suc an analysis is to analyze te effect of 665

14 different attacks on te proposed approac. However, in tis article for te sake of brevity & witout sacrificing any understandability, only te mean values of te proposed tecnique are included for calculation. Acronym used in te Table 3 indicates te mean values tat are obtained by considering all te attacks except te attack represented by te acronym. For instance, column 3 depicts te mean values obtained for te image by considering all attacks except brigtness adjustment (BA). Oter acronyms are: contrast adjustment (CA), Cropping (Crop), Gamma correction (GC), Gaussian low-pass filtering (GLPF), Gamma correction (GN), JPEG compression (JPEG), Median filter (MF), Rescaling (RE), Rotation (RO), Salt and pepper noise (S&P), Speckle noise (SPK), Sifting-H(S-H), Sifting- V(S-V), Sifting-HV(S-HV) and Searing (Sea). To make a fair comparison of te obtained mean values, four state-of-te-art tecniques are referenced. Table 3 represents te mean values obtained by te proposed approac. Similarly, we obtained te mean values by using te tecniques reported by (Tang et al., 214b), (Tang et al., 213a), (Tang et al., 28) and (Ou et al., 29) respectively. It is important ere to empasize tat tere may be sligt difference between te values reported by (Tang et al., 214b), (Tang et al., 213a), (Tang et al., 28), (Ou et al., 29) and te values obtained by us. Tis is because te dataset used by us is different as compared to te dataset used by te reported tecniques. Also approac adopted to generate duplicate copies of original ones also differs. However, in our experiment in order to make a fair comparison among all te reported tecniques and te proposed tecnique, tey are evaluated on te same dataset. Terefore, te comparison ere can be correctly used to draw any findings from te calculated results. Te difference is calculated one by one between te mean values given in te first column and te remaining 16 columns. Finally, te averaging of different values is done to produce te row vector, wic corresponds to te values related to different attacks. Tis calculation is done for all te tecniques and is given in Table 4. It was found tat te calculated values ave varying ranges for eac of te referenced tecniques. Terefore, to perform a fair comparison between tem, te values are normalized witin te range of to 1. Te normalized average values of te proposed tecnique along wit te tecniques reported in (Tang et al., 214b), (Tang et al., 213a), (Tang et al., 28) and (Ou et al., 29) are given in Table 5 and Fig. 5 plots tese normalized values. Te normalized values of Sifting orizontally (SF- H), Sifting orizontally-vertically (SF-HV), Searing (Sea) and Sifting vertically (SF-V) corresponding to te proposed tecnique are.484,.661,.784, and.57 respectively. It is quite evident from te values given in Table 5 tat te proposed tecnique exibits lowest average values for all te sifting and searing attacks. 666

15 Table 3 Mean values of as distances under different attacks for te proposed tecnique Image All Attacks BA CA Crop GC GLPF GN JPEG MF RE RO S&P SPK SF-H SF- HV Sea SF-V Airplane Barbara Boat Couple ;.36 Goldill Lena Peppers Sailboat Splas Zelda

16 Table 4 Average mean values difference of compared tecniques for different attacks Tecnique BA CA CROP GC GLPF GN JPEG MF RE RO SP SPK SF-H SF-HV SHEA SF-V [Tang et al., b] [Tang et al., a] [Tang et al., 28] [Ou et al., 29] Proposed Table 5 Normalized average mean values difference of compared tecniques for different attacks Tecnique BA CA CROP GC GLPF GN JPEG MF RE RO SP SPK SF-H SF-HV SHEA SF-V [Tang et. al, b] [Tang et. al, 213a] [Tang et al., 28] [Ou et al., ] Proposed

17 1.4 [Tang et al., 214b] [Tang et al., 213a] [Tang et al., 28] [Ou et al., 29] 1.2 Proposed 1. NAMVD Figure 5. Normalized average mean difference values for different tecniques. Performance Comparison wit State-of-te-art Tecniques Performance comparison of te proposed tecnique wit te state-of-te-art tecniques is also done in terms of robustness and discriminability by using ROC curve. Te tecniques compared wit include (Tang et al., 214b), (Tang et al., 213a), (Tang et al., 28) and (Ou et al., 29). In (Tang et al., 214b), te images were pre-processed by converting to a dimension of image, application of Gaussian filtering and ten converted to YCbCr for as generation. In (Tang et al., 213a) te image is resized to , followed by color conversion to YCbCr and HSI color models. In (Tang et al., 28), te image is resized to followed by gray-scale conversion for as generation. In (Ou et al., 29), te images are resized to followed by conversion to YCbCr and application of 5 5 Gaussian filtering to generate te final image wic is used for as generation. To represent te performance in terms of robustness and discriminability, te receiver operating caracteristics (ROC) curve is employed wic is usually plotted between te true positive rate (TPR) and te false positive rate (FPR). Tese parameters are defined as: TPR n N 1 1 FPR n N (1) were n1 is te number of visually identical images correctly identified as copies and N1 is te total number of identical images. Similarly, n2 is te number of different images incorrectly identified as a copy and N2 is te total

18 number of different images. TPR and FPR can be used to evaluate te robustness and te discriminability respectively. If two algoritms exibit te same TPR, ten te algoritm wit te lower FPR is considered as better performing. Similarly, if two algoritms exibit te same FPR, ten te algoritm wit te iger TPR is considered to be better performing. In order to draw te ROC curve, it is important to calculate te above parameters for varying tresolds. In general, a small tresold will improve te discrimination but simultaneously decreases te robustness. Te ROC curve for different algoritms including te proposed is given in Fig. 6. Te various tresolds used for producing te ROC for all te algoritms are given in Table 6. From Fig. 6, it is evident tat te ROC curve of te proposed tecnique is closer to zero as compared to te tecniques reported in Tang et al. (214b), Tang et al. (213a), Tang et al. (28) and Ou et al. (29). Te value of TPR wen FPR = in case of Tang et al. (214b), Tang et al. (213a), Tang et al. (28) and Ou et al. (29) is.61,.85,.38,.3 respectively wile for te proposed tecnique te value is.93. Similarly, te value of te FPR wen TPR = 1 in case of (Tang et al., 214b), (Tang et al., 213a), (Tang et al., 28) and (Ou et al., 29) is.91,.21, 1.,.98 respectively wile for te proposed tecnique te value is.13. Taking into account te values of te robustness and te discriminability from te previous subsection, along wit te TPR and FPR values obtained in tis subsection, it is quite clear tat te proposed asing tecnique outperforms some of te notable asing tecniques. 1. ROC Curve TPR [Tang et al., 214b] [Tang et al., 213a] [Tang et al., 28] [Ou et al., 29] Proposed FPR Figure 6. ROC curve comparison between proposed and oter asing algoritms. 67

19 Table 6 Tresolds used for generating ROC curves of different algoritms Algoritm Tresold [Tang et al., 214b] [Tang et al., 213a] [Tang et.al., 28] [Ou et.al., 29] Proposed Running Time Te running time of te proposed algoritm is analyzed by generating an image as of 2 different images. Te image ases are generated by using a computer aving an Intel Pentium Core-2-Duo processor wit a clock frequency of 1.8 GHz and 4GB of RAM. Te MATLAB version used was R214b. Te average time for as generation as reported in Tang et al. (214b), Tang et al. (213a), Tang et al. (28) and Ou et al. (29) is.24,.28, 1.14,.5 seconds respectively wile for te proposed algoritm it is.22 seconds. It is evident from te Table 7 tat te execution time of te proposed tecnique is smallest as compared to few of te notable algoritms. Table 7 Summary of execution time for different algoritms Mecanism Total Time (in sec) No. of Images Time per Image (in sec) [Tang et al., 214b] [Tang et al., 213a] [Tang et al., 28] [Ou et al., 29] Proposed Distribution of Has Distance To evaluate te distribution of te Has distance, two sets of image datasets were employed. One for te similar images and te oter for dissimilar images. To produce te dataset of similar images, 225 unique images are taken like 671

20 Airplane, Baboon, Lena in addition to images from te 17 Category Flower dataset (17 Category). For eac of tese images, 6 copies are generated using different image processing attacks to produce a set of 135 similar images. Te attacks applied include rotation, rescaling, Gaussian noise, brigtness adjustment etc. Similarly, for te dataset of dissimilar images, 135 different images are taken from te 17 Category Flower dataset (17 Category). After arranging te images in te dataset, Has distance is calculated by using te proposed algoritm. Te distribution of te Has distance for bot similar and dissimilar images is given in Fig. 7 and Fig. 8 respectively. Here, te tresold value used is.78, as it is identified during te robustness and te discrimination analysis. It is evident from tese figures tat te Has distance between similar images is below tresold of.78, wit a few exceptions. More specifically, out of 135 similar images 135 images return a Has distance less tan te tresold i.e % of te total images witin te dataset return a Has distance below te tresold. Correspondingly, most of te dissimilar images return a Has distance well above te tresold i.e. out of 135 different images, 58 images return as distances below te tresold. Terefore, we can say tat 4.29% different images are identified as similar ones. Tis analysis proves te efficacy of te proposed approac. Also, te number of outliers in bot te categories of (similar and dissimilar) images conforms to te true positive and false negative analysis performed for evaluating robustness and discrimination. 5 4 Frequency Has distance Figure 7. Distribution of as distance for similar images. 672

21 2 16 Frequency Has distance Figure 8. Distribution of as distance for different images. Comparison between Different Variants of Wavelet Transform Implementing a asing tecnique based on te DWT requires te calculation of wavelet coefficients at different levels. Any cange in te level of DWT would cange te corresponding coefficients. Tis section verifies te effect of DWT by calculating te as for different levels of DWT, wereas keeping te rest of te parameters constant. It is important ere to specify tat te proposed tecnique makes use of single level of 2D DWT. To demonstrate te effect of tis variation, a set of 3 images were taken from 17 Category Flower dataset (17 Category). A furter 88 copies were produced from a single image after applying various image processing operation (Table 1) leading to a total dataset size of 388 images. Initially, single level 2D DWT is applied in te proposed algoritm for finding duplicate copies of te original image. In te next iteration, two level 2D DWT is used for finding te duplicate copies. Tis procedure is repeated until we reac te level six of 2D DWT. To effectively represent results of tis analysis, ranking of te results based on te Has distance is done. Ranking is basically used to represent te order in wic multiple copies of te single image are found in te large dataset of images. For copy detection, ideally we require tat te rank at wic all te copies are found must be equal to te number of copies, i.e. te copied images sould be represented at top wit low rank and te non-copied images sould ave iger rank as compared to copied one. Te rank of te first 83 copies of te original image at one, two, tree, four, five and six levels of DWT 673

22 are 83, 9, 15, 1, 196 and 193 respectively. We can easily draw conclusion from te given values tat at lower level of DWT lower rank is generated and at te iger level it generates iger rank. Specifically, at level one we obtained te lowest rank of 83 among all te compared levels. Terefore, te best performance of te proposed tecnique can be obtained wen one level 2D DWT is used for as generation and comparison. It is important ere to clarify tat result of 88 copies is not considered in tis analysis as due to some outliers we are getting iger ranks for all te levels of DWT. However, suc result conforms to te results obtained for 83 copies. Te representation of ranks is sown in Fig Rank Rank Value Level of DWT Figure 9. Ranking of results based upon as distance for different level. CONCLUSION In tis paper, we ave presented a robust image asing tecnique tat employs Discrete Wavelet Transform and Houg transform to generate an image as, wic is used to differentiate te duplicate copies of images from teir original ones. Many experiments ave been conducted to validate te performances of te proposed asing. Normalized average mean value difference (NAMVD) is calculated to sow tat te proposed tecnique sows remarkable robustness to various sifting operations apart from performing well for oter content preserving operations like rotation, contrast adjustment. Compared wit four standard algoritms, te proposed tecnique acieves better performance in terms of ROC curves, wic clearly sows tat proposed tecnique is aving better classification in terms of robustness and 674

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