Multimedia Fingerprinting Research Articles

Modeling and Analysis of Correlated Binary Fingerprints for Content Identification

Multimedia identification via content fingerprints is used in many applications, such as content filtering on user-generated content websites, and automatic multimedia identification and tagging. A compact “fingerprint” is computed for each multimedia signal that captures robust and unique properties of the perceptual content, which is later used for identifying the multimedia. Several different multimedia fingerprinting schemes have been proposed in the literature and have been evaluated through experiments. To complement these experimental evaluations and provide guidelines for choosing system parameters and designing better schemes, this paper develops models for content fingerprinting and provides an analysis of the identification performance under these models. As a first step, bounds on the identification accuracy and the required fingerprint length for the simplest case when the fingerprint bits are modeled as i.i.d. are summarized. Markov Random Fields are then used to address more realistic settings of fingerprints with correlated components. The optimal likelihood ratio detector is derived and a statistical physics inspired approach for computing the probability of detection and probability of false alarm is described. The analysis shows that the commonly used Hamming distance detection criterion is susceptible to correlations among fingerprint bits, whereas the optimal log-likelihood ratio decision rule yields 5-20% improvement in the accuracy over a range of correlations. Simulation results demonstrate the validity of the theoretical predictions.

On Anti-Collusion Codes and Detection Algorithms for Multimedia Fingerprinting

Multimedia fingerprinting is an effective technique to trace the sources of pirate copies of copyrighted multimedia information. AND anti-collusion codes can be used to construct fingerprints resistant to collusion attacks on multimedia contents. In this paper, we first investigate AND anti-collusion codes and related detection algorithms from a combinatorial viewpoint, and then introduce a new concept of logical anti-collusion code to improve the traceability of multimedia fingerprinting. It reveals that frameproof codes have traceability for multimedia contents. Relationships among anti-collusion codes and other structures related to fingerprinting are discussed, and constructions for both AND anti-collusion codes and logical anti-collusion codes are provided.

Game-theoretic strategies and equilibriums in multimedia fingerprinting social networks

Multimedia social network is a network infrastructure in which the social network users share multimedia contents with all different purposes. Analyzing user behavior in multimedia social networks helps design more secured and efficient multimedia and networking systems. Multimedia fingerprinting protects multimedia from illegal alterations and multiuser collusion is a cost-effective attack. The colluder social network is naturally formed during multiuser collusion with which colluders gain reward by redistributing the colluded multimedia contents. Since the colluders have conflicting interest, the maximal-payoff collusion for one colluder may not be the maximal-payoff collusion for others. Hence, before a collusion being successful, the colluders must bargain with each other to reach agreements. We first model the bargaining behavior among colluders as a noncooperative game and study four different bargaining solutions of this game. Moreover, the market value of the redistributed multimedia content is often time-sensitive. The earlier the colluded copy being released, the more the people are willing to pay for it. Thus, the colluders have to reach agreements on how to distribute reward and risk among themselves as soon as possible. This paper further incorporates this time-sensitiveness of the colluders' reward and studies the time-sensitive bargaining equilibrium. The study in this paper reveals the strategies that are optimal for the colluders; thus, all the colluders have no inventive to disagree. Such understanding reduces the possible types of collusion into a small finite set.

Impact of Social Network Structure on Multimedia Fingerprinting Misbehavior Detection and Identification

Users in video-sharing social networks actively interact with each other, and it is of critical importance to model user behavior and analyze the impact of human factors on video sharing systems. In video-sharing social networks, users have access to extra resources from their peers, and they also contribute their own resources to help others. Each user wants to maximize his/her own payoff, and they negotiate with each other to achieve fairness and address this conflict. However, some selfish users may cheat to their peers and manipulate the system to maximize their own payoffs, and cheat prevention is a critical requirement in many social networks to stimulate user cooperation. It is of ample importance to design monitoring mechanisms to detect and identify misbehaving users, and to design cheat-proof cooperation stimulation strategies. Using video fingerprinting as an example, this paper analyzes the complex dynamics among colluders during multiuser collusion, and explores possible monitoring mechanisms to detect and identify misbehaving colluders in multiuser collusion. We consider two types of colluder networks: one has a centralized structure with a trusted ringleader, and the other is a distributed peer-structured network. We investigate the impact of network structures on misbehavior detection and identification, propose different selfish colluder identification schemes for different colluder networks, and analyze their performance. We show that the proposed schemes can accurately identify selfish colluders without falsely accusing others even under attacks. We also evaluate their robustness against framing attacks and quantify the maximum number of framing colluders that they can resist.

Décodage EM du code de Tardos pour le fingerprinting

This paper presents our recent work on multimedia fingerprinting, also known as traitor tracing. We focus on deriving a better accusation process for the well known Tardos codes. It appears that Tardos orginal decoding is very conservative: its performances are guaranteed whatever the collusion strategy. Indeed, major improvements stem from the knowledge of the collusion strategy. Therefore, this paper investigates how it is possible to learn and adapt to the collusion strategy. Our solution is based on an iterative algorithm a la EM, where a better estimation of the collusion strategy yields a better tracing of the colluders, which in return yields a better estimation of the collusion strategy etc.

Behavior forensics with side information for multimedia fingerprinting social networks

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> In multimedia social networks, there exists complicated dynamics among users who share and exchange multimedia content. Using multimedia fingerprinting as an example, this paper investigates the human behavior dynamics in the multimedia social networks with side information. Side information is the information other than the colluded multimedia content that can help increase the probability of detection. We study the impact of side information in multimedia fingerprinting and show that the statistical means of the detection statistics can help the fingerprint detector significantly improve the collusion resistance. We then investigate how to probe the side information and model the dynamics between the fingerprint detector and the colluders as a two-stage extensive game with perfect information. We model the colluder-detector behavior dynamics as a two-stage game and find the equilibrium of the colluder-detector game using backward induction and show that the min-max solution is a Nash equilibrium, which gives no incentive for everyone in the multimedia fingerprint social network to deviate. This paper demonstrates that the proposed side information can significantly help improve the system performance to almost the same as the optimal correlation-based detector. Such result opens up a new scope in the research of fingerprinting system that given any fingerprint code, leveraging side information can improve the collusion resistance. Also, we provide the solutions to how to reach optimal collusion strategy and the corresponding detection, thus lead to a better protection of the multimedia content. </para>

Behavior modeling and forensics for multimedia social networks

Factors influencing human behavior have seldom appeared in signal processing disciplines. Therefore, the goals of the article are to illustrate why human factors are important, identify emerging issues strongly related to signal processing, and to demonstrate that signal processing can be effectively used to model, analyze, and perform behavior forensics for multimedia social networks. Since media security and content protection is a major issue, the article illustrates various aspects of issues and problems in multimedia social networks via a case study of human behavior in traitor-tracing multimedia fingerprinting. We focus on the understanding of behavior forensics from signal processing perspective and present a framework to model and analyze user dynamics. The objective is to provide a broad overview of recent advances in behavior modeling and forensics for multimedia social networks.

Fingerprinting Codes for Multimedia Data against Averaging Attack

Code construction for digital fingerprinting, which is a copyright protection technique for multimedia, is considered. Digital fingerprinting should deter collusion attacks, where several fingerprinted copies of the same content are mixed to disturb their fingerprints. In this paper, we consider the averaging attack, which is known to be effective for multimedia fingerprinting with the spread spectrum technique. We propose new methods for constructing fingerprinting codes to increase the coding rate of conventional fingerprinting codes, while they guarantee to identify the same number of colluders. Due to the new fingerprinting codes, the system can deal with a larger number of users to supply digital contents.

AACS-compatible multimedia joint encryption and fingerprinting: Security issues and some solutions

In this paper, a new multimedia joint encryption and fingerprinting (JEF) scheme embedded into the advanced access content system (AACS) is proposed for multimedia transmission over networks. AACS is selected because it has been jointly developed by many famous companies and has been considered as the leading technology in content access control and multimedia distribution. In this framework, many attack points exist and can be exploited to defeat it. Furthermore, multiple attack points can be combined to form multi-point collusion attacks, which also endanger the proposed system. In this paper, we address the security concerns toward AACS-compatible JEF system in its entirety and propose solutions to cope with some security threats. The contributions of this paper include: (i) applying multimedia encryption at different points to resist some attacks points; (ii) proposing rewritable fingerprint embedding (RFE) to deal with some multi-point collusion attacks; (iii) designing a perceptual security spectrum metric (PSSM) to evaluate the degree of security when multiple encryptions are applied. The feasibility of the proposed AACS-compatible JEF method is further demonstrated through simulation results.

Markov Modelling of Fingerprinting Systems for Collision Analysis

Multimedia fingerprinting, also known as robust or perceptual hashing, aims at representing multimedia signals through compact and perceptually significant descriptors (hash values). In this paper, we examine the probability of collision of a certain general class of robust hashing systems that, in its binary alphabet version, encompasses a number of existing robust audio hashing algorithms. Our analysis relies on modelling the fingerprint (hash) symbols by means of Markov chains, which is generally realistic due to the hash synchronization properties usually required in multimedia identification. We provide theoretical expressions of performance, and show that the use of M-ary alphabets is advantageous with respect to binary alphabets. We show how these general expressions explain the performance of Philips fingerprinting, whose probability of collision had only been previously estimated through heuristics.

Adaptive Detection for Group-Based Multimedia Fingerprinting

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.

Collusion-Resistant Video Fingerprinting for Large User Group

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.

Concatenated construction of traceability codes for multimedia fingerprinting

We develop a framework of construction strategies for traceability codes on the subject of multimedia fingerprinting, which constructs traceability codes in a concatenated way. In fingerprinting systems, there are tradeoffs among the size of the customer base, the collusion resilience, and the codeword length. Our goal is to find a strategy to construct, traceability codes that reach an equilibrium among these tradeoffs. Instead of investigating a “high-performance” code, which has proven challenging in existing researches, we introduce a concatenated construction methodology to provide a flexible approach for producing more applicable traceability codes by composing several existing “low-performance” collusion-secure codes. Instead of just giving an example constructed by specific codes, we describe a general code construction strategy and give a detailed analysis on it. Moreover, we propose two concatenated construction schemes based on group fingerprinting to show the feasibility of the concatenated construction strategy. Experimental results show good performances on the code length, detection time, and tracing ability.

Content adaptive watermarking for multimedia fingerprinting

This paper presents a feasible system for multimedia fingerprinting. One of the important problems of fingerprinting is watermarking strategies for the fingerprints, in other words, where to embed and how to embed. We address these two major problems of fingerprint-watermarking for multimedia in the proposed content-adaptive watermarking scheme. First, the fingerprint codes are often very long if the applicable resisting ability to colluders and large customer bases are needed. As a result, the acute degradation of fingerprinted content may be unacceptable. We design a strength-decision algorithm, on the basis of neural networks, to adaptively embed the long-length fingerprints with suitable magnitudes to different image regions. This adaptive watermarking technique maintains the equilibrium of the robustness and the imperceptibility without the effort to deal with visual models. Second, we analyze the disturbance of collusion attacks on images and propose an optimization algorithm which can select better embedding positions to resist collusion attacks and preserve acceptable transparency of the watermark according to different multimedia contents. In addition, we consider the lossy property of multimedia watermarking and use a sequential detection strategy to identify colluders, which can tolerate erasures and errors possibly induced in the watermarking process or communication channel. Experimental results show the high detection correctness of traitor tracing. It implies that our fingerprinting system, constructed by applying c-TA code to the content-adaptive watermarking scheme and a sequential detection algorithm, is effective for multimedia application. One can replace the fingerprint codes in our system with other existing codes to obtain effective fingerprinting systems with higher tracing correctness and practical parameters (reasonable collusion-resilience and applicable size of customer bases).

Behavior forensics for scalable multiuser collusion: fairness versus effectiveness

Multimedia security systems involve many users with different objectives and users influence each other's performance. To have a better understanding of multimedia security systems and offer stronger protection of multimedia, behavior forensics formulate the dynamics among users and investigate how they interact with and respond to each other. This paper analyzes the behavior forensics in multimedia fingerprinting and formulates the dynamics among attackers during multi-user collusion. In particular, this paper focuses on how colluders achieve the fair play of collusion and guarantee that all attackers share the same risk (i.e., the probability of being detected). We first analyze how to distribute the risk evenly among colluders when they receive fingerprinted copies of scalable resolutions due to network and device heterogeneity. We show that generating a colluded copy of higher resolution puts more severe constraints on achieving fairness. We then analyze the effectiveness of fair collusion. Our results indicate that the attackers take a larger risk of being captured when the colluded copy has higher resolution, and they have to take this tradeoff into consideration during collusion. Finally, we analyze the collusion resistance of the scalable fingerprinting systems in various scenarios with different system requirements, and evaluate the maximum number of colluders that the fingerprinting systems can withstand

Fingerprint multicast in secure video streaming

Digital fingerprinting is an emerging technology to protect multimedia content from illegal redistribution, where each distributed copy is labeled with unique identification information. In video streaming, huge amount of data have to be transmitted to a large number of users under stringent latency constraints, so the bandwidth-efficient distribution of uniquely fingerprinted copies is crucial. This paper investigates the secure multicast of anticollusion fingerprinted video in streaming applications and analyzes their performance. We first propose a general fingerprint multicast scheme that can be used with most spread spectrum embedding-based multimedia fingerprinting systems. To further improve the bandwidth efficiency, we explore the special structure of the fingerprint design and propose a joint fingerprint design and distribution scheme. From our simulations, the two proposed schemes can reduce the bandwidth requirement by 48% to 87%, depending on the number of users, the characteristics of video sequences, and the network and computation constraints. We also show that under the constraint that all colluders have the same probability of detection, the embedded fingerprints in the two schemes have approximately the same collusion resistance. Finally, we propose a fingerprint drift compensation scheme to improve the quality of the reconstructed sequences at the decoder's side without introducing extra communication overhead.

Forensic analysis of nonlinear collusion attacks for multimedia fingerprinting

Digital fingerprinting is a technology for tracing the distribution of multimedia content and protecting them from unauthorized redistribution. Unique identification information is embedded into each distributed copy of multimedia signal and serves as a digital fingerprint. Collusion attack is a cost-effective attack against digital fingerprinting, where colluders combine several copies with the same content but different fingerprints to remove or attenuate the original fingerprints. In this paper, we investigate the average collusion attack and several basic nonlinear collusions on independent Gaussian fingerprints, and study their effectiveness and the impact on the perceptual quality. With unbounded Gaussian fingerprints, perceivable distortion may exist in the fingerprinted copies as well as the copies after the collusion attacks. In order to remove this perceptual distortion, we introduce bounded Gaussian-like fingerprints and study their performance under collusion attacks. We also study several commonly used detection statistics and analyze their performance under collusion attacks. We further propose a preprocessing technique of the extracted fingerprints specifically for collusion scenarios to improve the detection performance.

Collusion-secure and cost-effective detection of unlawful multimedia redistribution

Intellectual property protection of multimedia content is essential to the successful deployment of Internet content delivery platforms. There are two general approaches to multimedia copy protection: copy prevention and copy detection. Past experience shows that only copy detection based on mark embedding techniques looks promising. Multimedia fingerprinting means embedding a different buyer-identifying mark in each copy of the multimedia content being sold. Fingerprinting is subject to collusion attacks: a coalition of buyers collude and follow some strategy to mix their copies with the aim of obtaining a mixture from which none of their identifying marks can be retrieved; if their strategy is successful, the colluders can redistribute the mixture with impunity. A construction is presented in this paper to obtain fingerprinting codes for copyright protection which survive any collusion strategy involving up to three buyers (3-security). It is shown that the proposed scheme achieves 3-security with a codeword length dramatically shorter than the one required by the general Boneh-Shaw construction. Thus the proposed fingerprints require much less embedding capacity. Due to their own clandestine nature, collusions tend to involve a small number of buyers, so that there is plenty of use for codes providing cost-effective protection against collusions of size up to three.