Forensic analysis of nonlinear collusion attacks for multimedia fingerprinting

Abstract

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.

Behavior modeling and forensics for multimedia social networks

“…Detailed analysis of linear and nonlinear collusion attacks on orthogonal fingerprints was provided in [23]. The gradient attack was proposed in [24], which uses the combination of several basic nonlinear collusion attacks in [23].…”

“…The gradient attack was proposed in [24], which uses the combination of several basic nonlinear collusion attacks in [23]. The work in [25] evaluated the collusion resistance of multimedia fingerprints as a function of system parameters, including fingerprint length, total number of users, and system requirements.…”

Selfish Colluder Detection and Identification in Traitors within Traitors

“…Step 3 Step 4 Selfish Colluder Identification: Each colluder in SCh applies Algorithm 1 to estimate the identities of the selfish colluders.…”

“…During collusion, an important issue is the fairness of the attack, i.e., whether all colluders have the same probability of being detected. Most prior work assumed that all colluders keep their agreement to share the risk during collusion and focused on the analysis of collusion strategies and effectiveness [1][2][3][4].…”

Recent Fingerprinting Techniques with Cryptographic Protocol

“…Many of them can be categorized into two approaches. One is to exploit the Spread Spectrum (SS) technique Cox et al (1997); Wang et al (2004;; Zhao et al (2005), and the other approach is to devise an exclusive code, known as collusion-secure code Boneh & Shaw (1998); Staddon et al (2001); Tardos (2003); Trappe et al (2003); Yacobi (2001); Zhu et al (2005), which has traceability of colluders. Although cryptographic protocols provide the asymmetric property, the production of embedding information is based on the design of collusion-resistant fingerprint.…”