ArticlePloS one2013
Collusion-resistant audio fingerprinting system in the modulated complex lapped transform domain.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- A Blockchain and Fingerprinting Traceability Method for Digital Product Lifecycle Management.Sensors (Basel, Switzerland) · 2022Article
- A collusion-resistant fingerprinting system for restricted distribution of digital documents.PloS one · 2013Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Collusion-resistant fingerprinting paradigm seems to be a practical solution to the piracy problem as it allows media owners to detect any unauthorized copy and trace it back to the dishonest users. Despite the billionaire losses in the music industry, most of the collusion-resistant fingerprinting systems are devoted to digital images and very few to audio signals. In this paper, state-of-the-art collusion-resistant fingerprinting ideas are extended to audio signals and the corresponding parameters and operation conditions are proposed. Moreover, in order to carry out fingerprint detection using just a fraction of the pirate audio clip, block-based embedding and its corresponding detector is proposed. Extensive simulations show the robustness of the proposed system against average collusion attack. Moreover, by using an efficient Fast Fourier Transform core and standard computer machines it is shown that the proposed system is suitable for real-world scenarios.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.