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  1. 1. Vaishali V. Sarbhukan, Prof. V. B. Gaikwad / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 Vol. 2, Issue 6, November- December 2012, pp.1544-1548 TIRI – DCT Based Video Copy Detection System Vaishali V. Sarbhukan*, Prof. V. B. Gaikwad** *(Department of Computer Engineering, Mumbai University, Navi Mumbai) ** (Department of Computer Engineering, Mumbai University, Navi Mumbai)Abstract Nowadays thousands of videos are being Today’s widespread video copyright infringementuploaded to the internet and are shared every calls for the development of fast and accurate copy-day. Out of these videos, considerable numbers of detection algorithms. To detect infringements, therevideos are illegal copies or some videos are are two approaches. First is based on watermarkingmanipulated versions of existing media. Due to and other is based on content-based copy detectionthese reasons copyright management on the (CBCD).The watermarking is a widely usedinternet becomes a complicated process. technique in the photography field. It allows theCopyright infringement and data piracy are owner to detect whether the image has been copiedmajor serious concerns. To detect such kind of or not. The limitations [1] of watermarks are that ifinfringements, there are two approaches. First is the original image is not watermarked, then it is notbased on watermarking and other is based on possible to know if other images are copied or not.content-based copy detection (CBCD). Existing The primary aim of content-based copy detectionvideo fingerprint extraction algorithms like (CBCD) is “the media itself is the watermark”. Thecolor-space-based fingerprints, temporal key advantage of content-based copy detection overfingerprints, spatial fingerprints, and spatio- watermarking is the fact that the signature extractiontemporal fingerprints have certain limitations. can be done after the media has been distributed.Therefore temporally informative representativeimages - discrete cosine transform (TIRI-DCT) is 2. Strusture Of Fingerprinting Systemdeveloped. TIRI-DCT is based on temporally Fig. 1 shows the overall structure of thisinformative representative images which fingerprinting system. Content-Based Copycontains spatial and temporal information of a Detection finds the duplicate by comparing theshort segment of a video sequence.TIRI-DCT has fingerprint of the query video with the fingerprintsbetter performanance as compared to 3D-DCT. of the copyrighted videos. To find a copy of a queryTIRI-DCT followed by a fast approximate search video in a video database, one can search for a closealgorithms like inverted file-based similarity match of its fingerprint in the correspondingsearch and cluster-based similarity search. fingerprint database (extracted from the videos inExisting exhaustive search method is time the database). Closeness of two fingerprintsconsuming process. Drawback of exhaustive represents a similarity between the correspondingsearch method is overcome in inverted file-based videos; two perceptually different videos shouldsimilarity search and cluster-based similarity have different Extract Video FingerprintsKeywords – Cluster-based similarity search, 3D- DatabaseDCT, Inverted file-based similarity search,Fingerprinting system, TIRI-DCT.1. Introduction Fingerprint Security Database Due to growing broadcasting of digitalvideo content on different media brings the search (Optional)of copies in large video databases to a new criticalissue. Digital videos can be found on TV Channels,Web-TV, Video Blogs and the public Video Webservers. The massive capacity of these sourcesmakes the tracing of video content into a very hardproblem for video professionals. Recently forincreasing online video repositories copyright Extract Match? Query Searchinfringements and data piracy have become serious Fingerprint Videoconcerns. Copyright infringement occurs whensomeone other than the copyright holder copies the“expression” of a work. Copyright infringement isoften associated with the terms piracy and theft. Figure 1 Overall Structure of Fingerprinting System. 1544 | P a g e
  2. 2. Vaishali V. Sarbhukan, Prof. V. B. Gaikwad / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 Vol. 2, Issue 6, November- December 2012, pp.1544-15483. Properties of Fingerprints histograms of the colors in specific regions in time Ideally, a design of a video fingerprint and/or space within the video. According toshould have the following characteristics that hold Lienhart [2] the color coherence vector (CCV)true for a large corpus of video content of diverse differentiates between pixels of the same colortypes. depending on the size of the color region they belong to. Lienhart et al and Sanchez et al have3.1 Robustness been tested for the domain of TV commercials and Robustness of a fingerprint requires that it are susceptible to color variations. Naphade el alchanges as little as possible when the corresponding proposed a technique that was also experimented byvideo is subjected to content-preserving distortions. Hampapur el al. Naphade[3][4] propose to use YUV histograms as the signature of each frame in the3.2 Discriminant sequence and the use of histogram intersection as a The video fingerprints for different video distance measure between two signatures (frames).content should be distinctly different. First disadvantage of color-space-based fingerprint is that color features change with different video3.3 Easy to compute formats. Another drawback of color features is that The fingerprint should also be easy to they are not applicable to black and white videos.compute. For online applications, a fingerprintingalgorithm should be able to extract the signatures as 4.2Temporal fingerprintsthe video is being uploaded. To overcome drawback of color-space- based fingerprints new video fingerprint extraction3.4 Compact algorithm is developed that can be applied to the If a fingerprint is not compact, finding a luminance (the gray level) value of the frames.match for it in a very large database can become a According to Indyk and Shivkumar[5], first, a videotime-consuming process. sequence is segmented into shots. Then, the duration of each shot is taken as a temporal signature, and the3.5 Secure sequence of concatenated shot durations form the The fingerprinting system should be fingerprint of the video. Temporal signatures aresecured, so as to prevent an adversary from computed on adjacent frames in a video. Temporaltampering with it. fingerprints are extracted from the characteristics of a video sequence over time. These features usually3.6 Low complexity work well with long video sequences, but do not The algorithm for extracting video perform well for short video clips since they do notfingerprints should have low computational contain sufficient discriminant temporalcomplexity so that a video fingerprint can be information.computed fast. 4.3 Spatial fingerprints3.7 Efficient for matching and search Spatial fingerprint algorithm converts a Although there are generic algorithms that video image into YUV color space in which thetreat all fingerprints as a string of bits in matching luminance (Y) component is kept and theand search, a good design of video fingerprint chrominance components (U, V) are discarded.should facilitate approximation and optimization to Spatial fingerprints are features derived from eachimprove the efficiency in matching and search. frame or from a key frame. They are widely used for both video and image fingerprinting. Spatial4. Traditional Video Fingerprint Extraction fingerprints can be further subdivided into globalAlgorithms and local fingerprints. Global fingerprints focus on Existing methods for CBCD usually extract the global properties of a frame or a subsection of ita small number of features like signatures or like image histograms, while local fingerprintsfingerprints from images or a video stream and then usually represent local information around somematch them with the database according to a interest points within a frame like edges, corners,dedicated voting function. Different types of etc. One shortcoming of spatial fingerprints is theirtraditional video fingerprint extraction algorithms inability to capture the video’s temporalare color-space based fingerprints, temporal information, which is an important discriminatingfingerprints, spatial fingerprints, and spatio- factor.Therefore spatio-temporal fingerprints aretemporal fingerprints. developed.4.1 Color-space-based fingerprints 4.4 Spatio-temporal fingerprints Color-space-based fingerprints are among Spatio-temporal fingerprints that containthe first feature extraction methods used for video both spatial and temporal information about thefingerprinting. They are mostly derived from the video are thus expected to perform better than 1545 | P a g e
  3. 3. Vaishali V. Sarbhukan, Prof. V. B. Gaikwad / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 Vol. 2, Issue 6, November- December 2012, pp.1544-1548 fingerprints that use only spatial or temporal informative representative images (TIRIs). In TIRI- fingerprints. Some spatio-temporal algorithms DCT before extracting the fingerprints, the input consider a video as a three-dimensional (3-D) matrix video signal is processed. Copies of the same video and extract 3-D transform-based features[6][7]. with different frame sizes and frame rates usually exist in the same video database. As a result, a 5. Proposed TIRI-DCT System fingerprinting algorithm should be robust to changes There are some disadvantages of existing fingerprint in the frame size as well as the frame rate. Down- extraction systems .They are as follows sampling[8] can increase the robustness of a • 3-D transform to a video is a computationally fingerprinting algorithm to these changes. Prior to demanding process. down-sampling, a Gaussian smoothing filter is • The computational bottleneck is the search time in applied in both domains to prevent aliasing. This the matching process rather than the fingerprint down-sampling process provides the fingerprinting extraction time. algorithm with inputs of fixed size (W X H) pixels • Overlapping reduces the sensitivity of the and fixed rate (F frames/second). After fingerprints to the “synchronization problem” . The preprocessing, the video frames are divided into problem with the binarization scheme limits the overlapping segments of fixed-length, each number of coefficients. containing J frames. The fingerprinting algorithms These drawbacks are overcome in proposed are applied to these segments. Overlapping reduces Temporally Informative Representative Images- the sensitivity of the fingerprints to the Discrete Cosine Transform (TIRI-DCT) system. As “synchronization problem” which is called as “time a Temporally informative representative Images shift”. As TIRI-DCT transform algorithm capture of (TIRI) contains spatial and temporal information of the temporal information in a video using the a short segment of a video sequence, the spatial feature extraction process. TIRI-DCT Algorithm feature extracted from a TIRI would also contain includes following steps temporal information. Based on TIRIs; we propose Step 1: Generate TIRIs from each segment of J an efficient fingerprinting algorithm called as frames after preprocessing of input video .TIRIs are Temporally Informative Representative Images- generated using . Discrete Cosine Transform (TIRI-DCT.TIRI-DCT is Step 2: Segment each TIRI into overlapping blocks improved version of 3D-DCT. of size , using TIRIPreprocessing Where and Weighted Generate Average Blocks When indexes are outside of boundary then TIRI image is padded with 0’s. Step 3: Extract DCT coefficient from each TIRI block. These are first horizontal and first vertical DCT coefficient. First vertical frequency can Extract 1st Extract 1st be found for as horizontal DCT Vertical DCT Coefficient Coefficient Where Concatenate And 1 is column vector of all ones. Similarly first horizontal frequency can be found for as Spatio-temporal features Step 4: Concatenate all coefficients to form feature Binary vector f. fingerprints Step 5: Find median m, using all elements of f. Threshold Step 6: Generate binary hash h, using f Figure 2 Schematic of the TIRI-DCT algorithm Fig. 2 shows the block diagram of our proposed approach which is based on temporally 1546 | P a g e
  4. 4. Vaishali V. Sarbhukan, Prof. V. B. Gaikwad / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 Vol. 2, Issue 6, November- December 2012, pp.1544-15486. Search methods for fast matching of video This process continues until a match is found or thefingerprints farthest cluster is examined. In real-world applications, the size ofonline video databases can reach tens of millions of 7. Conclusionvideos, which translates into a very large fingerprint Due to increasing online video repositories,database size. This means that even if the fingerprint copyright infringements and data piracy haveof a query video can be extracted very quickly, become serious concerns .Many videos uploaded onsearching the fingerprint database to find a match internet are illegal copies or manipulated versions ofmay take a long time. For online applications, existing media. So today’s widespread videohowever, a reliable match should be found in almost copyright infringement calls for the development ofreal-time. Therefore fingerprint matching forms a fast and accurate copy-detectionpractical bottleneck for online fingerprinting algorithms.Temporally Informative Representativesystems. A simple exhaustive search method which Images (TIRI) - Discrete Cosine Transform (DCT)has a complexity of , where is the number extracts compact content- based signatures from special images constructed from the video. Eachof the fingerprints in the database. Search methods image in TIRI represents a short segment of theinverted file-Based Similarity Search and Cluster- video and contains temporal as well as spatialBased Similarity Search methods are modified information about the video segment. To findversion of search algorithm that was proposed by whether a query video (or a part of it) is copied fromOostveen [9]. a video in a video database, the fingerprints of all the videos in the database are extracted and stored in6.1 Inverted-File-Based Similarity Search advance. The search algorithm searches the stored The steps involved in Inverted-File-Based fingerprints to find close enough matches for theSimilarity Search are as follows: fingerprints of the query video. The disadvantagesStep 1: Binary fingerprints are divided into n words of existing fingerprint extraction systems areof equal bits. overcome in Proposed TIRI-DCT. TIRI-DCT isStep 2: The horizontal dimension of the table based on temporally informative representativerepresents the position of a words. Images. As a Temporally informative representativeStep 3: The vertical dimension of the table Images (TIRI) contains spatial and temporalrepresents the possible values of words. information of a short segment of a video sequence,Step 4: Add index for each word of the fingerprint the spatial feature extracted from a TIRI would alsoto the entry in column corresponding to the value of contain temporal information.TIRI-DCT is fasterthe word. than 3D-DCT while maintaining a very goodStep 5: Hamming distance is calculated between performance over the range of the consideredfingerprints in database and query fingerprint. attacks. TIRI-DCT decreases false rejection rateStep 6: If the distance is less than threshold value, (FPR) by increasing length of fingerprint. Anotherthen the query video will be announced as matching. important property of TIRI-DCT is that it isStep 7: Otherwise, it will be announced as not computationally less demanding than 3D-DCT.matching. TIRI-DCT followed by a fast approximate search algorithms like inverted file-Based Similarity Search6.2 Cluster-Based Similarity Search and Cluster-Based Similarity Search. These search Cluster-Based Similarity Search is another methods have better performance as compared tosimilarity search algorithm for binary fingerprints. existing exhaustive search method. Proposed TIRI-Our main idea is to use clustering to reduce the DCT, Inverted-File-Based Similarity Search andnumber of queries that are examined within the Cluster-Based Similarity Search algorithms willdatabase. By assigning each fingerprint to one and improve performance by producing a high averageonly one cluster (out of clusters), the fingerprints true positive rate (TPR) and a low average falsein the database will be clustered into positive rate (FPR).nonoverlapping groups. To do so, a centroid ischosen for each cluster, termed the cluster head. A REFERENCESfingerprint will be assigned to cluster if it is closest [1] J. Law-To, L. Chen, A. Joly, I. Laptev, this cluster’s head . To determine if a query Buisson, V. Gouet-Brunet, N. Boujemaa,fingerprint matches a fingerprint in the database, the and F. Stentiford, Video copy detection: Acluster head closest to the query is found. All the comparative study, in Proc. Conf. Imagefingerprints (of the videos in the database) Video Retrieval (CIVR), 2007.belonging to this cluster are then searched to find a [2] C. K. R. Lienhart and W. Effelsberg, Onmatch, i.e., the one which has the minimum the detection and recognition of televisionHamming distance (of less than a certain threshold) commercials, in Proc. of the IEEE Conf. onfrom the query. If a match is not found, the cluster Multimedia Computing and Systems, 1997.that is the second closest to the query is examined. 1547 | P a g e
  5. 5. Vaishali V. Sarbhukan, Prof. V. B. Gaikwad / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 Vol. 2, Issue 6, November- December 2012, pp.1544-1548[3] A. Hampapur and R. Bolle ,Comparison of sequence matching techniques for video copy detection, in Conference on Storage and Retrieval for Media Databases, 2002.[4] Naphade, M. R., Yeung, M. M. and Yeo, B.-L., A novel scheme for fast and efficient video sequence matching using compact signatures, Proc. SPIE, Storage and Retrieval for Media Databases, vol. 3972, Jan. 2000.[5] P. Indyk, G. Iyengar, and N. Shivakumar. Finding pirated video sequences on the internet, Technical report, Stanford University, 1999.[6] B. Coskun, B. Sankur, and N. Memon, Spatiotemporal transform based video hashing, IEEE Trans. Multimedia, vol. 8, no. 6, Dec. 2006.[7] Radhakrishnan and C. Bauer, Robust video fingerprints based on Subspace embedding,in Proc. ICASSP, Apr. 2008.[8] Mani Malek Esmaeili, Mehrdad Fatourechi and Rabab Kreidieh Ward ,Robust and Fast Video Copy Detection System Using Content Based Fingerprinting, IEEE Trans On Information Forensics And Security,Vol.6 No.1,March 2011.[9] J. Oostveen, T. Kalker, and J. Haitsma, Feature extraction and a database strategy for video fingerprinting, in Proc. Int. Conf. Recent Advances in Visual Information Systems (VISUAL), London, U.K., 2002, Springer-Verlag. 1548 | P a g e