Video Copy Detection Method Research Articles

On-Chain Video Copy Detection Based on Swin-Transformer and Deep Hashing

In recent years, short videos are spreading faster and become higher quality due to edge-cloud technology. People receive information gradually from graphic to video. At the same time as the number of videos spread rapidly, infringing videos are also flooding the Internet. The wild spread of infringing videos on the Internet has brought serious losses to video websites and original authors. Although video copy detection can solve such problems, the detection results are easy to be tampered with, and the detection results are hardly convincing. Based on this, this paper proposes an open, transparent and verifiable video copy detection method, which uses blockchain technology to ensure the transparency and openness of the results. In addition, this method adopts the combination of on-chain and off-chain methods to automatically perform copyright detection by invoking smart contracts on the chain. This mechanism can securely and immutably store video feature values on the blockchain, ensuring the originality of copyrighted works and the ability to verify detection results. Swin-Transformer and deep hashing are used to obtain video features off the blockchain, which can efficiently match the similarity of existing videos. The method of block comparison can greatly shorten the comparison time, which is 1/50 of the traditional comparison time. Experimental results show that this method is very effective in retrieving similar images and detecting the similarity between original and pirated videos.

Ring decomposition based video copy detection using global ordinal measure features and local features

Visual hashing-based or fingerprinting-based video copy detection approach has been adopted numerously by the video search community due to significant escalation of manipulated copies of original videos over the Internet. Most of the existing video copy detection approaches are robust against the content-preserving distortions such as brightness enhancement and compression, but less robust against the geometric distortions such as rotation and scaling. To mitigate the problem of computation overhead is still challenging in video copy detection. Moreover, there exist a trade-off between discriminability and robustness properties in most of the existing copy detection approaches. In this paper, an effective and fast video copy detection method is presented by exploiting both spatial-temporal information to tackle the above-mentioned challenges. The novelty of proposed method lies in reducing the computation overhead by generating an intermediate candidate database that are similar to the query video using ring-based Ordinal Measure (OM). Then, distinct visual features based on Histogram of Oriented Gradient (HOG) and Singular Value Decomposition (SVD) are extracted from each key-frame of every scenes of the videos of an intermediate candidate database and a query video for copy detection. To avoid the creation of redundant key-frames, the video frames are grouped into different scenes based on Discrete Cosine Transform (DCT). To further preserve the spatial-temporal information, the Temporally Informative Representative Image (TIRI) is used to generate each key-frame of every scenes of videos. The experimental result shows that the proposed method is more efficient and robust against various distortions which outperforms the state-of-the-art copy detection approaches.

Geometrically robust video hashing based on ST-PCT for video copy detection

Copy videos flooding in the network infringe the video copyright and heavy the storage pressure on the video services server, which raises a huge demand for video copy detection techniques that can accurately and quickly detect the copies of video from huge video database. This paper aims to generate a video hash which has not only high discrimination but also robustness against geometrical and spatial-temporal transformations. First, we propose Spatial-Temporal Polar Cosine Transform (ST-PCT), which considers a video as a three-dimensional matrix and performs two-dimensional Polar Cosine Transforms (PCT) after performing a one-dimensional Discrete Cosine Transform (DCT) on video. This transformation can extract features of the spatial-temporal domain and has geometric invariance. Then, based on ST-PCT, we propose a geometrically robust video hashing method for video copy detection. The video features generated by ST-PCT are compressed and quantified to a compact binary hash code. Experimental results show that compared with the state-of-the-art methods, the proposed method has better robustness, higher accuracy, and faster calculation speed.

Video Copy Detection Based on Deep CNN Features and Graph-Based Sequence Matching

This paper introduces a novel content-based video copy detection method using the deep CNN features. An efficient deep CNN feature is employed to encode the image content while retaining the discrimination capability. Taking advantage of the extremely fast Euclidean distance similarity of deep CNN features, a keyframe-based copy retrieval method that exhaustively searches the copy candidates from the large keyframe database without indexing is proposed. Moreover, a graph-based sequence matching algorithm is employed to obtain the copy clips and accurately locate the video segments. The experimental evaluation has been performed to show the efficacy of the proposed deep CNN features. The promising results demonstrate the effectiveness of our proposed approach.

Parallel Implementation Strategy for Content Based Video Copy Detection Using a Multi-core Processor

Video copy detection methods have emerged in recent years for a variety of applications. However, the lack of efficiency in the usual retrieval systems restricts their use. In this paper, we propose a parallel implementation strategy for content based video copy detection (CBCD) by using a multi-core processor. This strategy can support video copy detection effectively, and the processing time tends to decrease linearly as the number of processors increases. Experiments have shown that our approach is successful in speeding up computation and as well as in keeping the performance.

SIFT and DWT based Video Sequence Matching Method for Videocopy Detection

This paper introduces SIFT and DWT based video sequence matching method for video copy detection. Since local features have good stability and discriminative ability SIFT descriptors are used here for video content description. Content matching using SIFT takes large amount of time and it is computationally expensive for high dimensions and large number of points. These difficulties are solved by using dual threshold method which divides videos into segments having homogeneous content and by performing keyframe extraction on each of these segments. SIFT features are then extracted from these keyframes and SIFT feature sets of two video frames are matched using SVD based method. It has the problem of high processing time proportional to the length of video content. So we proposed DWT based fingerprint generation technique to reduce the processing time. Fingerprints of videos are generated and fingerprint matching is performed in the preprocessing step. So based on these results, it decides whether the SIFT feature matching has to be performed or not. Experimental results shows that SIFT and DWT based video sequence matching method for video copy detection can effectively detect video copies. Proposed system has following advantages such as, based on the spatial features it can effectively find optimal sequence matching result from the disordered matching results, it can effectively reduce the processing time and it is adaptive to video frame rate changes. Experimental results also demonstrate that the proposed method can obtain a better tradeoff between the effectiveness and the efficiency of video copy detection. KeywordsVideo copy Detection, Scale Invariant Feature Transform, Singular Value Decomposition, Discrete wavelet transform.

Efficient video copy detection using multi-modality and dynamic path search

Efficient and robust video copy detection is an important topic for many applications, such as commercial monitoring and social media retrieval. In this paper, with the aim of handling large-scale video data, we propose an efficient and robust video copy detection method jointly utilizing the characteristics of temporal continuity and multi-modality of video. The video is converted to a continuous sequence of states, and both the visual and auditory features are extracted for temporal frames. To facilitate tolerance of the length variations caused during video re-targeting, an efficient dynamic path search method is proposed to detect the target video clips, and highly compact audio fingerprint and visual ordinal features are jointly utilized in a flexible frame. The proposed scheme not only achieves high computational efficiency but also guarantees effectiveness in real applications. Comparison experiments were conducted using video commercials and real television programs from four channels as well as a benchmark video copy detection dataset, and the results demonstrate both the high efficiency and high robustness of the proposed method.

The System to Retain Procurement of Video Data and Eradicate UN trusted User’s

In this paper a secure mechanism to protect the intellectual property of the content creator of the websites like Google video, you tube, yahoo. Here the server will identify the unauthorized user and block that user from the network. By using the video copy detection methods compare the user video and admin videos to detect whether the trained video and queried videos may copies are not. Initially water marking techniques are used to protect the intellectuality of admin, but water marking itself is fragile for compression. Then content based copy detection (CBCD) proposed, it has a signature limitation problem. So the local descriptor Scale Invariant Feature Transform (SIFT) introduce to extort the features from the key frames. It encounters a computational cost. Then Singular Value Decomposition (SVD) is proposed for match two frames of video by using SIFT descriptor. Auto dual brink process is used to eradicate the superfluous video frames. And the graph based process is used to obtain video sequence and identify the longest path of matching result along with timing constraint. Then the queried video is the copy then that particular user blocked based on time stamp Keywords: Blocking, Intellectual property, video copy, Superfluous frames, Time Stamp.

A Segmentation and Graph-Based Video Sequence Matching Method for Video Copy Detection

We propose in this paper a segmentation and graph-based video sequence matching method for video copy detection. Specifically, due to the good stability and discriminative ability of local features, we use SIFT descriptor for video content description. However, matching based on SIFT descriptor is computationally expensive for large number of points and the high dimension. Thus, to reduce the computational complexity, we first use the dual-threshold method to segment the videos into segments with homogeneous content and extract keyframes from each segment. SIFT features are extracted from the keyframes of the segments. Then, we propose an SVD-based method to match two video frames with SIFT point set descriptors. To obtain the video sequence matching result, we propose a graph-based method. It can convert the video sequence matching into finding the longest path in the frame matching-result graph with time constraint. Experimental results demonstrate that the segmentation and graph-based video sequence matching method can detect video copies effectively. Also, the proposed method has advantages. Specifically, it can automatically find optimal sequence matching result from the disordered matching results based on spatial feature. It can also reduce the noise caused by spatial feature matching. And it is adaptive to video frame rate changes. Experimental results also demonstrate that the proposed method can obtain a better tradeoff between the effectiveness and the efficiency of video copy detection.

Video Copy Detection Based on Fusion of Spatio-Temporal Features

A video copy detection method based on fusion of spatio-temporal features is proposed in this paper. Firstly, trajectories are built and lens boundaries are detected by SURF features analyzing, then normalized histogram is used to describe spatio-temporal behavior of trajectories, the bag of visual words is constructed by trajectories behavior clustering, word frequency vectors and SURF features with behavior labels are extracted to express spatio-temporal content of lens, finally, duplicates are detected efficiently based on grade-match. The experimental results show the performance of this method is improved greatly compared with other similar methods.

A Study on Video Copy Detection Methods Using Representative Color Sequence for Protecting Copyrights

This study deals with the technical approach related to the protection of digital video contents copyright, which has currently become a major issue in the digital contents market. With the production and distribution of digital video contents rapidly increasing, various policies and technical approaches are being made in relation to the protection of intellectual property rights. The technical approach from a DRM (Digital Rights Management) perspective was made from various angles, but its limitations have also been exposed in terms of convenience and quality. In this study, rather than the existing encoding certification and watermark technologies, it introduces technical approaches from a content-based copy detection method, while suggesting a copy detection method using the representative color sequence. In particular, this study takes into consideration the distribution environment of the domestic video market and deals with the technical approach in a direction that can control the illegal distribution of high quality videos.

Audio-visual large-scale video copy detection

With the development of hardware and proliferation of Web 2.0, an increasing need to provide efficient access of desired content in large-scale database has emerged. Content-based copy detection (CBCD) has attracted more research interests due to its broad applications. However, most existing CBCD approaches only focus on single view (visual view or audio view). In this paper, we attempt to address the CBCD task by considering both visual and audio information simultaneously. A bag of visual concept words model for video copy detection method is proposed from visual cue, and the main advantage of the proposed method lies in the fact that the presence of semantic concepts is highly robust to spatial and temporal video transformations. On the other hand, a bag of audio words model is presented from audio cue and a coherency vocabulary combined with soft-weighted strategy realizes fast and accurate indexing. Finally, the late fusion is adopted to obtain the final audio–visual result based on visual-only and audio-only results. Intensive experiments are conducted on two large-scale data sets and competitive results are achieved.

A LLE-based Video Copy Detection Method

A LLE-based Video Copy Detection Method

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.

Spatiotemporal Video Copy Detection Based on Visual Perception Analyses

A novel spatiotemporal video copy detection method based on visual perception features is proposed in this paper.The method differs from the conventional video copy detection algorithms in that it does not using key frame based features any more.By extracting visual perception based spatiotemporal correlation of slice in frequency domain using DCT,the proposed method possesses both robustness and discriminability for video copy detection.It copes well with not only the format,resolution variation of video but also the aspect ratio change.Even more,the method is robust to display format conversion such as adding pillar-box.The experiment results show the proposed method is effective and robust.

Research of content-based video copy detection

A novel content-based video copy detection method was proposed. As video information application had a strong temporal redundancy, a key-frame selection algorithm was presented, and the ordinal measure of the AC coefficients of its Discrete Cosine Transform (DCT) was used as its feature, then the L1 norm distance was computed for the similarity measure of the video. Results show that our method obtains a significant performance against several kinds of geometric transforms.