The content explores the vulnerability of iris recognition systems to dictionary attacks at the template level. By mixing IrisCodes using simple bitwise operators, alpha-wolves (from wolves) and alpha-mammals (from users selected via search optimization) are created to increase false matches. The study evaluates this vulnerability on various datasets, showing that these alpha-mixtures can match a high number of identities. The research also delves into related work, motivations behind the study, contributions made, experimental design, results and analysis, discussion on alpha-mixtures behavior, additional analysis like image translation viability check and synthetic IrisCode usage in attacks. The conclusion highlights the efficacy of these attacks and future directions for research.
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arxiv.org
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