Over the last three years, mobile handsets have evolved from voice based services to multimedia terminals that is gradually leading towards a new era of consumer entertainment fostered by mobile communications and consumer networks. The need for Mobile Digital Rights Management (MDRM) solutions is thus intensified in order to safeguard mobile media content. Various types of content protection solutions have been proposed. In this paper, we investigate current status, the standardization effort, and the challenges in the development of MDRM technologies. Sample state-of-the-art media security technologies for MDRM are also discussed.
KEYWORDS: Digital watermarking, Data hiding, Multimedia, Error control coding, Video, Wireless communications, Error analysis, Receivers, Telecommunications, Detection and tracking algorithms
Wireless channels are error prone communication channels. It is well known that multimedia data can be vulnerable to transmission errors, to various degrees. Error control, such as forward error control (FEC) and automatic repeat request (ARQ,) and error concealment techniques have been developed to combat transmission errors for robust multimedia communications. The efficiency and effectiveness of an error recovery technique rely on the system error detection capabilities. We review techniques proposed in the area of error detection via data hiding in this paper. Typical errors can be classified into several categories. We discuss conventional error types, review algorithms using data hiding for transmission error detection developed in the last several years, and propose future work directions, especially for robust wireless multimedia communication.
Wireless environments present many challenges for secure multimedia access, especial streaming media. The availability of varying network bandwidths and diverse receiver device processing powers and storage spaces demand scalable and flexible approaches that are capable of adapting to changing network conditions as well as device capabilities. To meet these requirements, scalable and fine granularity scalable (FGS) compression algorithms were proposed and widely adopted to provide scalable access of multimedia with interoperability between different services and flexible support to receivers with different device capabilities. Encryption is one of the most important security tools to protect content from unauthorized use. If a medium data stream is encrypted using non-scalable cryptography algorithms, decryption at arbitrary bit rate to provide scalable services can hardly be accomplished. If a medium compressed using scalable coding needs to be protected and non-scalable cryptography algorithms are used, the advantages of scalable coding may be lost. Therefore scalable encryption techniques are needed to provide scalability or to preserve the FGS adaptation capability (if the media stream is FGS coded) and enable intermediate processing of encrypted data without unnecessary decryption. In this paper, we will give an overview of scalable encryption schemes and present a fine grained scalable encryption algorithm. One desirable feature is its simplicity and flexibility in supporting scalable multimedia communication and multimedia content access control in wireless environments.
The development and spread of multimedia services require authentication techniques to prove the originality and integrity of multimedia data and (or) to localize the alterations made on the media. A wide variety of authentication techniques have been proposed in the literature, but most studies have been primarily focused on still images. In this paper, we will mainly address video authentication. We first summarize the classification of video tampering methods. Based on our proposed classification, the quality of existing authentication techniques can be evaluated. We then propose our own authentication system to combat those tampering methods. The comparison of two basic authentication categories, fragile watermark and digital signature, are made and the need for combining them are discussed. Finally, we address some issues on authenticating a broad sense video, the mixture of visual, audio and text data.
KEYWORDS: Data hiding, Digital watermarking, Video, Distortion, Digital imaging, Sensors, Image quality, Video compression, Interference (communication), Error analysis
Previous works on data hiding generally targeted on a specific tradeoff between capacity and robustness. This results in overestimation of the processing noise under some situations and/or underestimation under some other situations, hence limits the overall performance. In this paper, we propose a multi-level data hiding scheme which is able to convey secondary data in high rate when noise is not severe and can also convey some data reliably under heavy distortion. The proposed scheme is motivated by a two- category classification of embedding schemes and by a study on detection performance of spread spectrum watermarking. The multi- level data hiding has been successfully applied to both digital image and video, and can be used for applications such as copy control.
Video segmentation is an important first step towards automatic video indexing, retrieval, editing, and etc. However, the 'large' property of video makes it hard to handle in real time. To fulfill the goal of real-time processing, several factors need to be considered. First of all, indexing video directly in the compressed-domain offers the advantages of fast processing upon efficient storage. Secondly, extracting simple features with fast algorithms is no doubt helpful in speeding up the process. The questions are what kind of simple feature can characterize the changing statistics and what kind of algorithm can provide such feature with fast executability. In this paper, we propose a new automatic video segmentation scheme that utilizes wavelet transformation based on the following consideration: wavelet is a nice tool for subband decomposition, it encodes both frequency and spatial information; more over, it is easy to program and fast to execute. In the last decade or so, wavelet transform is emerged to image/video signal processing for analyzing functions at different levels of details. In particular, wavelet, as a tool, has been widely used in the area of image compression. In image compression, it is possible to recover a fairly accurate representation of the image by saving the few largest wavelet coefficients (and throwing away part or all of the smaller coefficients). By using this property, we extract a discrimination signature of each image from a few large coefficients for each color channel. The system works on the compressed video that does not require full decoding of the video and performs a wavelet transformation on the extracted video data. The signature (as feature) is extracted from the wavelet coefficients to characterize the changing statistics of shot transitions. Cuts, fades, and dissolve are detected based on the analysis of the changing statistics curve.
This paper describes a new fade and dissolve detection methodology that utilizes wavelet transformation. This approach takes advantage of the production aspects of video as well as mimicking human perception. Each frame of the video is first decomposed into low-resolution component and high- resolution component using wavelet transformation. The possible gradual changes are first detected with edge spectrum average (ESA) feature which is obtained from the high- resolution component, in the mean time, the changing statistics of the ESA is studied to identify fades. Double chromatic difference is applied later on the low-resolution component to identify the dissolve transitions.
The problem of scenic image classification is presented in the paper. On considering the specific nature of this problem, we propose a statistically data-based method, the Hidden Markov Model, to solve this problem. We segment an image and use the sequence of segments as the definition of the image; we then train a HMM on a test set of sequences/images to establish a classification. We present preliminary results on the use of a 1D HMM for classification of images as either indoor or outdoor.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.