Video Copy Detection System Research Articles

Video copy detection by conducting fast searching of inverted files

Fast content-based video copy detection is challenging because video databases have become extremely large. Conventional video-fingerprint-based copy detection systems that use the inverted-file approach involve many similarity computations based on the Hamming distance. To overcome this problem, a novel fast searching strategy for inverted files is proposed in this paper. The strategy involves simple table look-up and word counting operations for the fingerprint matching process. The similarity of video fragments is based on the number of matched fingerprints among all video candidates. In this method, the offset time is used, and fingerprints are ordered to further select the matched fingerprints from the video candidates. Moreover, a novel regional average fingerprint that is compatible with the proposed fast searching strategy is proposed. An experimental video copy detection system was used with the proposed algorithms, and the proposed algorithms were compared with other state-of-the-art fingerprinting algorithms on TRECVID 2011 dataset for different types of video distortions. In addition, VCDB dataset was also used to demonstrate the accuracy and efficiency of the proposed fast searching strategy while using inverted files to demonstrate the practicality of the method for a large database. The proposed system achieved higher accuracy on VCDB dataset with considerably higher operation speed compared with conventional inverted-file-based searching methods.

An Improved Method for Eliminating False Matches in Content-Based Video Copy Detection System

An Improved Method for Eliminating False Matches in Content-Based Video Copy Detection System

Weber Binarized Statistical Image Features (WBSIF) based video copy detection

This paper presents a new video copy detection system based on testing similarities between textural feature vectors which are extracted from videos. Herein, the proposed method is based on Weber Binarized Statistical Image Features (WBSIF) which is an interior improvement of Weber Local Descriptor (WLD). Actually, the orientation gradient in WLD is substituted by a recent Binarized Statistical Image Features (BSIF) as a local textural descriptor. The WBSIF approach is tested on three databases and evaluated through several attacks. Moreover, the proposed method is compared to the recent existing approaches, especially those mostly used in the literature, which are based on the binary pattern descriptors. The obtained results outline the robustness and the effectiveness of the proposed video copy detection system in terms of precision, recall, Fscore, accuracy and collision test. This study shows clearly a noteworthy performance of the proposed scheme against currently existing techniques.

Video Copy Detection Based on Path Merging and Query Content Prediction

Content-based video copy detection is undoubtedly one of the most effective solutions to video content tracing and copyright protection. It extracts features from videos and determines whether a copy occurs by comparing the extracted features. While a lot of work has been reported to address this problem with good performance, very few considered it from the perspective of a dynamic searching process. In this paper, we treat the copy detection in video streams as a sequential matching problem and take into consideration the connections between temporary results and forthcoming input. Specifically, we propose a video copy detection system that involves a novel frame fusion scheme and an adaptive search strategy. The proposed frame fusion scheme relies on path merging in a graph model, which is able to work in an online manner and provide informative temporary fusion results. Based on these temporary results, query content predictions can be generated, which will be fed back to the frame search engine to instruct it to adaptively adjust the search strategy. The experimental results show that the proposed frame fusion scheme achieves competitive detection and localization accuracies compared with the state-of-the-art methods. Meanwhile, with the assistance of the adaptive search strategy, the computational complexity of frame similarity search is dramatically reduced at a cost of a slight decrease in accuracy.

이중 영역 이진 패턴을 이용한 회전된 비디오 검출

Due to a number of illegal copied videos, many video content markets have been threatened. Since this copied videos have intercepted the profits of the content holders, content developers lose the will to generate new contents. Therefore, video copy detection approaches have been developed to protect the copyrights of video contents. However, many illegal uploader who generate copied videos used video transformations to avoid video copy detection systems. Among of the video transformations, rotation and flipping did not distorted the quality of video contents. Thus, these two video transformations were adopt to generate copied video. In order to detect rotated or flipping copy videos, rotation and flipping robust region binary pattern (RFR) recently was proposed. But, this RFR has a weakness according to rotated angles. Therefore, in order to overcome this problem, multi region binary patterns are proposed in this paper. The proposed method has the similar performance with the original RFR. But, it showed much higher efficiency for memory spaces.

FLIP-INVARIANT VIDEO COPY DETECTION USING SPARSE-CODED FEATURES

Now a days, a number of videos are available in video databases, social networking sites and other web servers. Large size of these video database make it difficult to trace the video content. To ensure the copy-right of the videos in video database, a video copy detection system is needed. A Video copy detection system stores the video features that characterize a video along with the video in the database. Existing copy detection systems store the video features as simple codewords. A simple and compact representation of video features makes the system more efficient. Moreover, the memory constraint problem can also be solved. This paper propose a sparse-coding technique that can represent the video features as sparse-codes. Proposed video copy detection system using sparse-codes works as follows: keyframes of the videos in the database are extracted using abrupttransition detection algorithm. Salient regions of keyframes are detected by Harris-Laplacian detector and its local features are described by Flip-Invariant SIFT(F-SIFT) descriptor. F-SIFT enriches SIFT with flip invariance property by preserving its feature distinctiveness. F-SIFT is invariant to operations like flip, rotation, scale etc. A 128-Dimensional F-SIFT descriptor is extracted from each salient region. Extracted descriptors are converted to sparse-codes by the proposed sparse-coding technique. Each keyframe is represented by the sparse feature vector. Sparse vectors of all the keyframes of a video forms the sparse code of the video. Sparse-codes of the input video are compared with the sparse-codes stored in video database to identify the near duplicate videos. Experimental results demonstrate that proposed sparse-coding technique reduces the memory constraint problem. It also improves the detection accuracy.

Adaptive weighted fusion with new spatial and temporal fingerprints for improved video copy detection

In this paper, we propose a new and novel modality fusion method designed for combining spatial and temporal fingerprint information to improve video copy detection performance. Most of the previously developed methods have been limited to use only pre-specified weights to combine spatial and temporal modality information. Hence, previous approaches may not adaptively adjust the significance of the temporal fingerprints that depends on the difference between the temporal variances of compared videos, leading to performance degradation in video copy detection. To overcome the aforementioned limitation, the proposed method has been devised to extract two types of fingerprint information: (1) spatial fingerprint that consists of the signs of DCT coefficients in local areas in a keyframe and (2) temporal fingerprint that computes the temporal variances in local areas in consecutive keyframes. In addition, the so-called temporal strength measurement technique is developed to quantitatively represent the amount of the temporal variances; it can be adaptively used to consider the significance of compared temporal fingerprints. The experimental results show that the proposed modality fusion method outperforms other state-of-the-arts fusion methods and popular spatio-temporal fingerprints in terms of video copy detection. Furthermore, the proposed method can save 39.0%, 25.1%, and 46.1% time complexities needed to perform video fingerprint matching without a significant loss of detection accuracy for our synthetic dataset, TRECVID 2009 CCD Task, and MUSCLE-VCD 2007, respectively. This result indicates that our proposed method can be readily incorporated into the real-life video copy detection systems.

A Robust Video Copy Detection System using TIRI-DCT and DWT Fingerprints

video copy detection system that is based on content fingerprinting can be used for video indexing and copyright applications. Most of the video copy detection algorithms proposed so far focus mostly on coping with signal distortions introduced by different encoding parameters; however, these algorithms do not cope well with display format conversions. They may rely on a fingerprint extraction algorithm followed by a fast approximate search algorithm. The fingerprint extraction algorithm extracts compact content-based signatures (11)-(14) from special images constructed from the video. Each such image represents a short segment of the video and contains temporal as well as spatial information (7), (8), (10) about the video segment. These images are denoted by temporally informative representative images (1). To find whether a query video is copied from a video in a video database, the fingerprints of all the videos in the database are extracted and stored in advance. The search algorithm searches the stored fingerprints to find close enough matches for the fingerprints of the query video. The content based fingerprint extraction process does not work to get a better level search. Further in an enhancement of this concept of fingerprints this system handles the TIRI-DCT and DWT features to detect the copyright information. This paper proposes a novel sequence matching technique to detect copies of a video clip. If a video copy detection technique is to be effective, it needs to be robust to the many digitization and encoding processes that give rise to several distortions, including changes in brightness, color, frame format, as well as different blocky artifacts. It also handles a new nonmetric distance measure to find the similarity between the query and a database video fingerprint and it is proposed to achieve accurate duplicate detection. Then the performance of the TIRI DCT and the co-efficient is compared. The proposed method has been extensively tested and the results show that the proposed scheme is effective in detecting copies which has been subjected to wide range of modifications.

Inverted File Based Search Technique for Video Copy Retrieval

A video copy detection system is a content-based search engine focusing on Spatio -temporal features. It aims to find whether a query video segment is a copy of video from the video database or not based on the signature of the video. It is hard to find whether a video is a copied video or a similar video since the features of the content are very similar from one video to the other.The main focus is to detect that the query video is present in the video database with robustness depending on the content of video and also by fast search of fingerprints. The Fingerprint Extraction Algorithm and Fast Search Algorithm are adopted to achieve robust, fast, efficient and accurate video copy detection. As a first step, the Fingerprint Extraction algorithm is employed which extracts a f ingerprint through the features from the image content of video. The images are represented as Temporally Informative Representative Images (TIRI).Then the next step is to find the presence of copy of a query video in a video database, in which a close m atch of its fingerprint in the corresponding fingerprint database is searched uinverted-file-based method.

Performance Analysis of Fingerprinting Extraction Algorithm in Video Copy Detection System

A video fingerprint is a recognizer that is derived from a piece of video content. The video fingerprinting methods obtain unique features of a video that differentiates one video clip from another. It aims to identify whether a query video segment is a copy of video from the video database or not based on the signature of the video. It is difficult to find whether a video is a copied video or a similar video, since the features of the content are very similar from one video to the other. The main focus of this paper is to detect that the query video is present in the video database with robustness depending on the content of video and also by fast search of fingerprints. The Fingerprint Extraction Algorithm and Fast Search Algorithms are adopted in this paper to achieve robust, fast, efficient and accurate video copy detection. As a first step, the Fingerprint Extraction algorithm is employed which extracts a fingerprint through the features from the image content of video. The images are represented as Temporally Informative Representative Images (TIRI). Then, the second step is to find the presence of copy of a query video in a video database, in which a close match of its fingerprint in the corresponding fingerprint database is searched using inverted-filebased method. The proposed system is tested against various attacks like noise, brightness, contrast, rotation and frame drop. Thus the performance of the proposed system on an average shows high true positive rate of 98% and low false positive rate of 1.3% for different attacks.

Spider: A System for Finding Illegal 3D Video Copies

Three-dimensional (3D) videos are getting quite popular, and equipment for recording and processing them are becoming affordable. Creating 3D videos is expensive. Thus, protecting 3D videos against illegal copying is an important problem. We present a novel system for finding 3D video copies. Our system also identifies the location of the copied part in the reference video. The system can be used, for example, by video content owners, video hosting sites, and third-party companies to find illegally copied 3D videos. To the best of our knowledge, this is the first complete 3D video copy detection system in the literature. Detecting 3D video copies is a challenging problem. First, comparing numerous numbers of frames from potential copies against reference videos is computationally intensive. Second, many modifications occur on copied videos; some of them are intentional to avoid detection and others are side effects of the copying process. For example, a copied video can be scaled, rotated, cropped, transcoded to a lower bit rate, or embedded into another video. The contrast, brightness, and colors of a video can also be manipulated. Furthermore, 3D videos come in various encoding formats, including stereo, multiview, video plus depth, and multiview plus depth. Changing the format is possible during copying, which complicates the detection process. Finally, new views can be synthesized from existing ones. These views display the scene from different angles, and thus reveal different information than in original views. For example, an object occluded in one view could appear in another. We implemented the proposed system and evaluated its performance using many 3D videos. We created a large set of query videos with 284 videos to represent all practical scenarios. Our results show that the proposed system achieves high precision and recall values in all scenarios. Specifically, our system results in 100% precision and recall when copied videos are unmodified parts of original videos, and it produces more than 90% precision and recall when copied videos are subjected to various transformations. Even in extreme cases where each video is subjected to five different transformations, our system yields more than 75% precision and recall.

A Robust and Fast Video Copy Detection System Using Content-Based Fingerprinting

A video copy detection system that is based on content fingerprinting and can be used for video indexing and copyright applications is proposed. The system relies on a fingerprint extraction algorithm followed by a fast approximate search algorithm. The fingerprint extraction algorithm extracts compact content-based signatures from special images constructed from the video. Each such image represents a short segment of the video and contains temporal as well as spatial information about the video segment. These images are denoted by temporally informative representative images. To find whether a query video (or a part of it) is copied from a video in a video database, the fingerprints of all the videos in the database are extracted and stored in advance. The search algorithm searches the stored fingerprints to find close enough matches for the fingerprints of the query video. The proposed fast approximate search algorithm facilitates the online application of the system to a large video database of tens of millions of fingerprints, so that a match (if it exists) is found in a few seconds. The proposed system is tested on a database of 200 videos in the presence of different types of distortions such as noise, changes in brightness/contrast, frame loss, shift, rotation, and time shift. It yields a high average true positive rate of 97.6% and a low average false positive rate of 1.0%. These results emphasize the robustness and discrimination properties of the proposed copy detection system. As security of a fingerprinting system is important for certain applications such as copyright protections, a secure version of the system is also presented.

An Image-Based Approach to Video Copy Detection With Spatio-Temporal Post-Filtering

This paper introduces a video copy detection system which efficiently matches individual frames and then verifies their spatio-temporal consistency. The approach for matching frames relies on a recent local feature indexing method, which is at the same time robust to significant video transformations and efficient in terms of memory usage and computation time. We match either keyframes or uniformly sampled frames. To further improve the results, a verification step robustly estimates a spatio-temporal model between the query video and the potentially corresponding video segments. Experimental results evaluate the different parameters of our system and measure the trade-off between accuracy and efficiency. We show that our system obtains excellent results for the TRECVID 2008 copy detection task.

통계적 분석 기반 불법 복제 비디오 영상 감식 방법

The carelessly and illegally copied contents are raising serious social problem as internet and multimedia technologies are advancing. Therefore, development of video copy detection system must be settled without delay. In this paper, we propose the hierarchical video copy detection method that estimates similarity using statistical characteristics between original video and manipulated(transformed) copy video. We rank according to luminance value of video to be robust to spacial transformation, and choose similar videos categorized as candidate segments in huge amount of database to reduce processing time and complexity. The copy videos generally insert black area in the edge of the image, so we remove rig black area and decide copy or not by using statistical characteristics of original video and copied video with center part of frame that contains important information of video. Experiment results show that the proposed method has similar keyframe accuracy to reference method, but we use less memory to save feature information than reference`s, because the number of keyframes is less 61% than that of reference`s. Also, the proposed method detects if the video is copied or not efficiently despite expansive spatial transformations such as blurring, contrast change, zoom in, zoom out, aspect ratio change, and caption insertion.