We investigate the image authentication system, SARI, proposed by C.Y. Lin and S.F. Chang (see SPIE storage and retrieval of image/videodatabases, 1998), that distinguishes JPEG compression from malicious manipulatio...
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We investigate the image authentication system, SARI, proposed by C.Y. Lin and S.F. Chang (see SPIE storage and retrieval of image/videodatabases, 1998), that distinguishes JPEG compression from malicious manipulations. In particular, we look at the image digest component of this system. We show that if multiple images have been authenticated with the same secret key and the digests of these images are known to an attacker, Oscar, then he can cause arbitrary images to be authenticated with this same but unknown key. We show that the number of such images needed by Oscar to launch a successful attack is quite small, making the attack very practical. We then suggest possible solutions to enhance the security of this authentication system.
This Volume 3312 of the conference proceedings contains 40 papers. Topics discussed include imageretrieval, video representation, video segmentation, intelligent tools, similarity search, image and video authenticati...
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This Volume 3312 of the conference proceedings contains 40 papers. Topics discussed include imageretrieval, video representation, video segmentation, intelligent tools, similarity search, image and video authentication and videostorage and delivery.
This book provides an in-depth treatment of the three important topics related to image and videodatabases: restoration, watermarking and retrieval . It is the result of the participation of the Delft University of ...
ISBN:
(数字)9780080508474
ISBN:
(纸本)9780444505026
This book provides an in-depth treatment of the three important topics related to image and videodatabases: restoration, watermarking and retrieval . It is the result of the participation of the Delft University of Technology in the European Union ACTS program, a pre-competitive R&D program on Advanced Communications Technologies and Services (1994-1998). In particular the book has benefited from participation in the AURORA and SMASH projects respectively automated film and video restoration and storage for multimedia systems (watermarking & retrieval).
Multimedia data are generally stored in compressed form in order to efficiently utilize the available storage facilities. Access to multimedia archives is thus dependent on our ability to browse compressed information...
Multimedia data are generally stored in compressed form in order to efficiently utilize the available storage facilities. Access to multimedia archives is thus dependent on our ability to browse compressed information. In this paper, a novel approach to multiple object tracking from compressed multimedia databases is presented. This approach is intended to operate in a distributed environment, where users initiate video searches and retrieve relevant video information simultaneously From multiple compressed video archives. The system operates on the compressed video to find and track objects of interest and determine their positions in the image. This enables more complex query formulations in terms of the relative positions of the target objects in the image. The filtering and analysis of motion information (motion vectors) is used to track objects in the video bit stream. Once the search has terminated. the system may decompress and display the query-relevant video sequences upon request. (C) 2000 Academic Press.
Histograms are the most prevalently used representation for the color content of images and video. An elaborate representation of the histograms requires specifying the color centers of the histogram bins and the coun...
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ISBN:
(纸本)0819435902
Histograms are the most prevalently used representation for the color content of images and video. An elaborate representation of the histograms requires specifying the color centers of the histogram bins and the count of the number of image pixels with that color. Such an elaborate representation, though expressive, may not be necessary for some tasks in image search, filtering and retrieval. A qualitative representation of the histogram is sufficient for many applications. Such a representation will be compact and greatly simplify the storage and transmission of the image representation. It will also reduce the computational complexity of search and filtering algorithms without adversely affecting the quality. We present such a compact binary descriptor for color representation. This descriptor is the quantized Haar transform coefficients of the color histograms. We show the use of this descriptor for fast retrieval of similar images and search for similar video segments from a large database. We also show the use of this descriptor for browsing large imagedatabases without the need for computationally expensive clustering algorithms. The compact nature of the descriptor and the associated simple similarity measure allows searching over a database of about four hours of video in less than 5-6 seconds without the use of any sophisticated indexing scheme.
With the advent of pervasive computing, a growing diversity of client devices is gaining access to audio-visual content. The increased variability in client device processing power, storage, bandwidth, and server load...
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ISBN:
(纸本)0780365364
With the advent of pervasive computing, a growing diversity of client devices is gaining access to audio-visual content. The increased variability in client device processing power, storage, bandwidth, and server loading require adaptive solutions for image, video and audio retrieval. Progressive retrieval is one prominent mode of access in which views at different resolutions are incrementally retrieved and refined over time. In this paper, we present a new framework for adaptively partitioning the synthesis operations in progressive retrieval of audio-visual signals. The framework considers that the server and client cooperate in synthesizing the views in order to best utilize the available processing power and bandwidth. We provide experimental results that demonstrate a significant reduction in latency in the progressive retrieval of images under different conditions of the client, server and network.
videodatabases are very demanding systems as far as mass storage requirements and computational resources necessary to perform common database operations, such as browsing and retrieval, are required. These operation...
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ISBN:
(纸本)0780362985
videodatabases are very demanding systems as far as mass storage requirements and computational resources necessary to perform common database operations, such as browsing and retrieval, are required. These operations can be simplified both in terms of computational complexity and of processing time by performing them on an ensemble of frames, called key frames, representative of the content units (shots) in which a video can be segmented. In this contribution an adaptive key frames extraction method based on a wavelet based multiresolution analysis in a perceptually uniform color space is presented. Experimental results that show the effectiveness of the proposed technique to select key frames summarizing the video's content, are finally provided.
In this work, we present a system for the automatic segmentation, indexing and retrieval of audiovisual data based on the combination of audio, visual and textual content analysis. The video stream is demultiplexed in...
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In this work, we present a system for the automatic segmentation, indexing and retrieval of audiovisual data based on the combination of audio, visual and textual content analysis. The video stream is demultiplexed into audio, image and caption components. Then, a semantic segmentation of the audio signal based on audio content analysis is conducted, and each segment is indexed as one of the basic audio types. The image sequence is segmented into shots based on visual information analysis, and keyframes are extracted from each shot. Meanwhile, keywords are detected from the closed caption. Index tables are designed for both linear and non-linear access to the video. It is shown by experiments that the proposed methods for multimodal media content analysis are effective, and that the integrated framework achieves satisfactory results for video information filtering and retrieval.
Due to the huge amount of potentially interesting documents available over the Internet, searching for relevant information has become very difficult. Since image and video are a major source of these data, grouping i...
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Due to the huge amount of potentially interesting documents available over the Internet, searching for relevant information has become very difficult. Since image and video are a major source of these data, grouping images into (semantically) meaningful categories using low-level visual features is an important (and challenging) problem in content-based imageretrieval. Using Bayesian classifiers, we attempt to capture high-level concepts from low-level image features. Specifically, we have developed Bayesian classifiers for semantic image classification (indoor vs. outdoor, city vs. landscape, and sunset vs. forest vs. mountain), image orientation detection, and object detection (detecting regions of sky and vegetation in outdoor images). We demonstrate that a small codebook (the optimal codebook size is selected using a modified MDL criterion) extracted from a learning vector quantizer can be used to estimate the class-conditional densities of the observed features needed for image classification. We have developed an incremental learning paradigm, a feature selection scheme, a rejection scheme, and a classifier combination strategy using bagging to improve classifier performance. Empirical results on a large database (∼24,000 images) show that semantic categorization and organization of the database using the proposed classification schemes improves both retrieval accuracy and efficiency.
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