In this work, we propose a joint audio-video fingerprint Automatic Content Recognition (ACR) technology for media retrieval. The problem is focused on how to balance the query accuracy and the size of fingerprint, and how to allocate the bits of the fingerprint to video frames and audio frames to achieve the best query accuracy. By constructing a novel concept called Coverage, which is highly correlated to the query accuracy, we are able to form a rate-coverage model to translate the original problem into an optimization problem that can be resolved by dynamic programming. To the best of our knowledge, this is the first work that uses joint audio-video fingerprint ACR technology for media retrieval with a theoretical problem formulation. Experimental results indicate that compared to reference algorithms, the proposed method has up to 25 query accuracy improvement while using 60 overall bit-rates, and 25 bit-rate reduction while achieving 85 accuracy, and it significantly outperforms the solution with single audio or video source fingerprint.
The research community has addressed the problem of ACR by two approaches: and . Watermarking is about inserting specific identifier and time-stamp information in the content which can be viewed later. Fingerprinting, on the other hand, analyzes and compares unique content characteristic with reference database. Fingerprinting does not require the source media to be modified in any way, enables service providers to prepare indexing system for any broadcast or TV programs with no change in the content creator’s or broadcaster’s work flow. Another advantage of fingerprinting is the additive property: unlike watermarking, fingerprinting requires no copyright to the media since it does not change the original content, making it possible to be broadly used across as many applications as possible. As a result, fingerprinting system is the most widely used content recognition technique in ACR. More specifically, fingerprints, also referred to as signatures or features, are intrinsic data to characterize the video content for indexing, searching, and ranking @cite_12 .
@cite_12
2157897932 2022303741 9496346 2039690308
In order to utilize peer-to-peer (P2P) networks in legal content distribution to benefit the legal content providers, copyright protection needs to be enhanced. In this paper, a fingerprint generation and embedding method is proposed for complex P2P file sharing networks. In this method, wavelet and principal component analysis (PCA) techniques are used for fingerprint generation. First, the wavelet technique obtains a low-frequency representation of the test image (or source file, which is assumed to be one I frame of a video with a DVD quality) and PCA finds the features of the representation. Then, a set of fingerprint matrices can be created based on a proposed algorithm. Finally, each matrix combines with the low-frequency representative to become a unique fingerprinted matrix. The fingerprinted matrix is not only much smaller than the original image in size but also contains the most important information. Without this information, the quality of the reconstructed image will be very poor. Thus, the fingerprinted file is more suitable for distribution in P2P networks, because, in the distribution stage, the uniquely fingerprinted matrix will only be dispensed by the source host and leave the rest for P2P networks to handle. On the other hand, among other frames of the same video which are not decomposed, some will be embedded with sharable fingerprints. The relationship between unique fingerprint and sharable fingerprint and the purpose of using it will be discussed in the paper. Our result indicates that the proposed fingerprint has shown strong robustness against common attacks such as Gaussian noise, median filter, and lossy compression. This paper presents an efficient approach for copies detection in a large videos archive consisting of several hundred of hours. The video content indexing method consists of extracting the dynamic behavior on the local description of interest points and further on the estimation of their trajectories along the video sequence. Analyzing the low-level description obtained allows to highlight trends of behaviors and then to assign a label of behavior to each local descriptor. Such an indexing approach has several interesting properties: it provides a rich, compact and generic description, while labels of behavior provide a high-level description of the video content. Here, we focus on video Content Based Copy Detection (CBCD). Copy detection is problematic as similarity search problem but with prominent differences. To be efficient, it requires a dedicated on-line retrieval method based on a specific voting function. This voting function must be robust to signal transformations and discriminating versus high similarities which are not copies. The method we propose in this paper is a dedicated on-line retrieval method based on a combination of the different dynamic contexts computed during the off-line indexing. A spatio-temporal registration based on the relevant combination of detected labels is then applied. This approach is evaluated using a huge video database of 300 hours with different video tests. The method is compared to a state-of-the art technique in the same conditions. We illustrate that taking labels into account in the specific voting process reduces false alarms significantly and drastically improves the precision. A method and system for recording and reproducing, such as on a record carrier, content information and supplemental information relating thereto. The content information may be audio and or video, and the supplemental information may provide author identification and or copy control status. An encoded signal is generated representing the content information and which includes a watermark pattern representing the supplemental information. The watermark pattern cannot be changed without impairing the quality of the content information during reproduction. The supplemental information also includes a control pattern, the watermark being generated by applying a one-way function to such control pattern. This has the advantage that any alteration of the watermark or the control pattern can be detected easily, because it is not computationally feasible to calculate a new control pattern for an altered watermark. Therefore, the supplemental information is well protected against unauthorized manipulation. An attempt to fully replace the watermark pattern will affect the quality of reproduction of the content information. In a copy control method allowing a first generation copy ("copy-once"), the original control pattern is processed several times by the one-way function for generating the watermark. Each player or recorder processes the control pattern once before outputting recording it, thus forming a cryptographically protected down-counter. Abstract.We enunciate the need for watermarking database relations to deter data piracy, identify the characteristics of relational data that pose unique challenges for watermarking, and delineate desirable properties of a watermarking system for relational data. We then present an effective watermarking technique geared for relational data. This technique ensures that some bit positions of some of the attributes of some of the tuples contain specific values. The specific bit locations and values are algorithmically determined under the control of a secret key known only to the owner of the data. This bit pattern constitutes the watermark. Only if one has access to the secret key can the watermark be detected with high probability. Detecting the watermark requires access neither to the original data nor the watermark, and the watermark can be easily and efficiently maintained in the presence of insertions, updates, and deletions. Our analysis shows that the proposed technique is robust against various forms of malicious attacks as well as benign updates to the data. Using an implementation running on DB2, we also show that the algorithms perform well enough to be used in real-world applications.