Digital fingerprinting has been widely used to protect multimedia content from being used for unauthorized purposes. Digital fingerprints are often embedded in the host media signal using watermarking techniques that are known to be resistant to a variety of processing attacks. However, one cost-effective strategy to attack digital fingerprints is collusion, where several colluders average their individual copies to disrupt the underlying fingerprints. Recently, a new class of fingerprinting codes, called anti-collusion codes (ACC), has been proposed for use with code-modulated data embedding. In designing digital fingerprints that are resistant to collusion attacks, there are several important design considerations: how can we accommodate as many users as possible for a given fingerprint dimensionality, and how can we identify the colluders effectively from the colluded signal? In this work, we identify an underlying similarity between the colluder detection problem and the multiuser detection problem from code division multiple access (CDMA). We propose that fingerprints can be constructed using sequence sets satisfying the Welch Bound Equality (WBE). WBE sequences have been shown to be optimal in synchronous CDMA. In order to identify the colluders when employing WBE-based ACC, we further propose a detection algorithm utilizing sphere decoding that identifies the colluders from the colluded signal. We evaluate the performance of the proposed WBE-based ACC fingerprints with our proposed detection algorithm through simulations, and show that the algorithm performs well at moderate noise levels. Finally, we compare our design scheme against orthogonal fingerprints and the BIBD
anti-collusion codes proposed earlier, and show that the proposed WBE-based ACC and detection algorithm have better performance than BIBD-based ACC under the same configuration.
Digital fingerprints are unique labels inserted in different copies of the same content before distribution. Each digital fingerprint is assigned to an inteded recipient, and can be used to trace the culprits who use their content for unintended purposes. Attacks mounted by multiple users, known as collusion attacks, provide a cost-effective method for attenuating the identifying fingerprint from each coluder, thus collusion poses a reeal challenge to protect the digital media data and enforce usage policies. This paper examines a few major design methodologies for collusion-resistant fingerprinting of multimedia, and presents a unified framework that helps highlight the common issues and the uniqueness of different fingerprinting techniques.
We propose a new key distribution scheme for multimedia multicast by exploiting the characteristics of multimedia signals. First, a basic rekeying message construction is presented which can be used to distribute rekeying material to a group of users using number theoretic techniques. We then map the basic rekeying form to a logical tree structure to achieve logarithmic scalability and discuss several key updating strategies. Furthermore, we present a general key distribution scheme using data embedding for multimedia multicast. The performance and system feasibility of the proposed scheme is also analyzed. We also extend the key management scheme for multilayer multimedia applications in heterogeneous networks where group members have different quality requirements.
The lifting scheme is an effective method that provides flexible solutions for designing new perfect reconstruction filter bands. However, most of the existing applications of lifting are based upon stationary assumptions. As such, existing schemes tend to fit the data with a single, pre- determined model. These methods do not exploit the full flexibility provided by lifting. By exploiting the temporal interpretation of lifting, we incorporate adaptive filtering with the lifting scheme to cope with signals whose characteristics vary with time. In this paper, we study the proposed adaptive lifting scheme and its ability to decorrelate subbands. The decorrelation behavior is related proposed adaptive lifting scheme and its ability to decorrelate subbands. The decorrelation behavior is related to the coherence between the subbands, and simulations indicate improved decorrelation when compared with deterministic lifting. Our adaptive filterbank may be used in a thresholding scheme that can yield improved noise reduction capabilities compared to conventional wavelet thresholding schemes. We present a condition under which the proposed adaptive lifting denoising scheme can outperform a similar wavelet thresholding. Simulations are presented that indicate there is an SNR value at which the performance of adaptive lifting denoising surpasses wavelet denoising.
A theory of frames that extend Gabor analysis by including chirping is discussed. The chirping parameter in these 'time-frequency localization frames' depends on time and/or frequency shift parameters that can be adapted to analyze and detect chirps in noisy signals. Radar/sonar applications are outlined. The frame theory is motivated by a generalized notion of square-integrable group representation developed by Ali, Antoine, and Gazeau, together with ideas in Baraniuk's thesis on a metaplectic extension of Cohen's class.