Digital fingerprinting is a technique to deter unauthorized redistribution of multimedia content by embedding a unique identifying signal in each legally distributed copy. The embedded fingerprint can later be extracted and used to trace the originator of an unauthorized copy. A group of users may collude and attempt to create a version of the content that cannot be traced back to any of them. As multimedia data is commonly stored in compressed form, this paper addresses the problem of fingerprinting compressed signals. Analysis is carried out to show that due to the quantized nature of the host signal and the embedded fingerprint, directly extending traditional fingerprinting techniques for uncompressed signals to the compressed case leads to low collusion resistance. To overcome this problem and improve the collusion resistance, a new technique for fingerprinting compressed signals called Anti-Collusion Dither (ACD) is proposed, whereby a random dither signal is added to the compressed host before embedding so as to make the effective host signal appear more continuous. The proposed technique is shown to reduce the accuracy with which attackers can estimate the host signal, and from an information theoretic perspective, the proposed ACD technique increases the maximum number of users that can be supported by the fingerprinting system under a given attack. Both analytical and experimental studies confirm that the proposed technique increases the probability of identifying a guilty user and can approximately quadruple the collusion resistance compared to conventional Gaussian fingerprinting.

High fidelity is a demanding requirement for data hiding, especially for images with artistic or medical value. This correspondence proposes a high-fidelity image watermarking for annotation with robustness to moderate distortion. To achieve the high fidelity of the embedded image, we introduce a visual perception model that aims at quantifying the local tolerance to noise for arbitrary imagery. Based on this model, we embed two kinds of watermarks: a pilot watermark that indicates the existence of the watermark and an information watermark that conveys a payload of several dozen bits. The objective is to embed 32 bits of metadata into a single image in such a way that it is robust to JPEG compression and cropping. We demonstrate the effectiveness of the visual model and the application of the proposed annotation technology using a database of challenging photographic and medical images that contain a large amount of smooth regions.

Financial efficiency is the premier performance measure for most systems. Existing economic ecosystems for distribution of multimedia leave a lot to be desired: client-server platforms do not scale well resulting in substantial operational costs, whereas peer-to-peer platforms cannot police copyright control and are thus notorious for not being able to capitalize on its vast delivery potential. In this paper, we introduce an economic model that aims at predicting financial performance of both client-server and viral distribution systems for multimedia. The model consists of several probabilistic components: a global scale-free viral network of users and a localized user-behavior model that abstracts marketing, pricing, and executed transactions. The model uses simulation to predict relative economic behavior. In order to showcase our model, we compared the popular "on-line store" distribution system to the recently proposed off-line incentive-based viral ecosystem for multimedia. We also constructed an efficient dynamic pricing scheme and evaluated its performance in considered multimedia distribution scenarios.

This paper considers protecting multimedia content from unauthorized redistribution in subscription based services, where adversaries work together to pirate multiple multimedia programs during a subscription period. Collusion-resistant fingerprinting is an emerging tool for traitor tracing. However, most of the existing fingerprinting works did not consider multiple rounds of interaction between collusion and detection. In this paper, we exploit the temporal dimension and propose a dynamic fingerprinting scheme that adjusts the fingerprint design based on the detection result of previously pirated signal. We also examine colluders' strategies to combat the tracing by dynamic fingerprinting. Both analytical and simulation results show that the proposed dynamic fingerprinting provides better collusion resistance than conventional static fingerprinting.

Digital fingerprinting is an emerging tool to protect multimedia content from unauthorized distribution by embedding a unique fingerprint into each user's copy. Although several fingerprinting schemes have been proposed in related work, disproportional effort has been targeted towards identifying effective collusion attacks on fingerprinting schemes. Recent introduction of the gradient attack has refined the definition of an optimal attack and demonstrated strong effect on direct-sequence, uniformly distributed, and Gaussian spread spectrum fingerprints when applied to synthetic signals. In this paper, we apply the gradient attack on an existing well-engineered video fingerprinting scheme, refine the attack procedure, and demonstrate that the gradient attack is effective on Laplace fingerprints. Finally, we explore an improvement on fingerprint design to thwart the gradient attack. Results suggest that Laplace fingerprint should be avoided. However, we show that a signal mixed of Laplace and Gaussian fingerprints may serve as a design strategy to disable the gradient attack and force pirates into averaging as a form of adversary collusion.

Most existing collusion-resistant fingerprinting techniques are for fingerprinting uncompressed signals. In this paper, we first study the performance of the traditional Gaussian based spread spectrum sequences for fingerprinting compressed signals and show that the system can be easily defeated by averaging or taking the median of a few copies. To overcome the collusion problem for compressed multimedia host signals, we propose a technique called anti-collusion dithering to mimic an uncompressed signal. Results show higher probability of catching a colluder using the proposed scheme compared to using Gaussian based fingerprints.

In this paper, we analyze the effect of various collusion attacks on fingerprinting systems for compressed multimedia. We evaluate the effectiveness of the collusion attacks in terms of the probability of detection and accuracy in estimating the host signal. Our analysis shows that applying averaging collusion on copies of moderately compressed content gives a highly accurate estimation of the host, and can effectively remove the embedded fingerprints. Averaging is thus the best choice for an attacker as the probability of detection and the distortion introduced are the lowest.

Grouping strategy has been proposed recently to leverage the prior knowledge on collusion pattern to improve collusion resistance in multimedia fingerprinting. However, the improvement is not consistent, as reduced performance of the existing group-based fingerprinting schemes than the non-grouped ones is observed when the grouping does not match the true collusion pattern well. In this letter, we propose a new adaptive detection method, where the threshold for the group detection can be adjusted automatically according to the detection statistics that reflect the underlying collusion pattern. Experimental results show that the proposed adaptive detection outperforms nonadaptive detection and provides consistent performance improvement over non-grouped orthogonal fingerprinting schemes under various collusion scenarios.

Digital fingerprinting protects multimedia content from illegal redistribution by uniquely marking copies of the content distributed to users. Most existing multimedia fingerprinting schemes consider a user set on the scale of thousands. However, in such real-world applications as video-on-demand distribution, the number of potential users can be as many as 10-100 million. This large user size demands not only strong collusion resistance but also high efficiency in fingerprint construction, and detection, which makes most existing schemes incapable of being applied to these applications. A recently proposed joint coding and embedding fingerprinting framework provides a promising balance between collusion resistance, efficient construction, and detection, but several issues remain unsolved for applications involving a large group of users. In this paper, we explore how to employ the joint coding and embedding framework and develop practical algorithms to fingerprint video in such challenging settings as to accommodate more than ten million users and resist hundreds of users' collusion. We investigate the proper code structure for large-scale fingerprinting and propose a trimming detection technique that can reduce the decoding computational complexity by more than three orders of magnitude at the cost of less than 0.5% loss in detection probability under moderate to high watermark-to-noise ratios. Both analytic and experimental results show a high potential of joint coding and embedding to meet the needs of real-world large-scale fingerprinting applications.

Digital fingerprinting protects multimedia content from illegal redistribution by uniquely marking copies of the content distributed to each user. Most existing multimedia fingerprinting schemes consider userset on the scale of thousands for evaluation. However, in real applications such as cable TV and DVD distribution, the potential users can be as many as 10~100 million. This large scale user set demands not only high collusion resistance but also high efficiency in the fingerprint construction and detection, which makes most existing schemes incapable of practical application. A recently proposed joint coding and embedding fingerprinting framework provides a promising balance between collusion resistance, efficient construction and detection. In this paper, we explore how to employ such a framework and develop practical algorithms to fingerprint video in such challenging practical settings as to accommodate more than ten million users and resisting hundreds of users' collusion. Both analysis and experimental results show the great potential of joint coding and embedding fingerprinting for real video fingerprinting applications.

In this paper we introduce a novel visual perception model that aims to quantify the localized tolerance to noise for arbitrary imagery. The model is built by mixing the outputs from entropy and a differential localized standard deviation filter. The mixture is then low-pass filtered and normalized to provide a model that produces substantially better perceptual hi-fidelity than existing tools of similar complexity. Although there exist numerous applications for the new model, from compression to medical imaging and denoising, we demonstrate its efficacy using an image annotation application. The objective is to embed 32 bits of meta-data into a single image in a way that is robust to aggressive JPEG compression and cropping. We demonstrate the effectiveness of the novel model as well as the developed annotation technology using a database of high-challenge images

Digital fingerprinting protects multimedia content from illegal redistribution by uniquely marking every copy of the content distributed to each user. The collusion attack is a powerful attack where several different fingerprinted copies of the same content are combined together to attenuate or even remove the fingerprints. One major category of collusion-resistant fingerprinting employs an explicit step of coding. Most existing works on coded fingerprinting mainly focus on the code-level issues and treat the embedding issues through abstract assumptions without examining the overall performance. In this paper, we jointly consider the coding and embedding issues for coded fingerprinting systems and examine their performance in terms of collusion resistance, detection computational complexity, and distribution efficiency. Our studies show that coded fingerprinting has efficient detection but rather low collusion resistance. Taking advantage of joint coding and embedding, we propose a permuted subsegment embedding technique and a group-based joint coding and embedding technique to improve the collusion resistance of coded fingerprinting while maintaining its efficient detection. Experimental results show that the number of colluders that the proposed methods can resist is more than three times as many as that of the conventional coded fingerprinting approaches.

Digital fingerprinting protects multimedia content from illegal redistribution by uniquely marking copies of the content distributed to each user. Collusion is a powerful attack whereby several differently fingerprinted copies of the same content are combined together to attenuate or remove the fingerprints. Focusing on the error correction code (ECC) based fingerprinting, we explore in this paper new avenues that can substantially improve its collusion resistance, and in the mean time retain its advantages in detection complexity and fast distribution. Our analysis suggests a great need of jointly considering the coding, embedding, and detection issues, and inspires the proposed technique of permuted subsegment embedding that is able to substantially improve the collusion resistance of ECC based fingerprinting.