Abstract
This paper describes Twizzle Benchmarking, a framework originally developed for evaluating and comparing the performance of perceptual image hashing algorithms. There are numerous perceptual hashing approaches with different characteristics in terms of robustness and sensitivity, which also use different techniques for feature extraction and distance measurements, making comparison difficult. For this reason, we have developed Twizzle Benchmarking, which enables comparison and evaluation regardless of the algorithm, distance calculation, data set or type of data. Furthermore, Twizzle is not limited to perceptual hashing approaches, but can be used for a variety of purposes and classification problems, such as multimedia forensics, face recognition or biometric authentication.
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Escher, S., Teufert, P., Herrmann, R., Strufe, T. (2020). Twizzle - A Multi-purpose Benchmarking Framework for Semantic Comparisons of Multimedia Object Pairs. In: Boureanu, I., et al. Computer Security. ESORICS 2020. Lecture Notes in Computer Science(), vol 12580. Springer, Cham. https://doi.org/10.1007/978-3-030-66504-3_13
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DOI: https://doi.org/10.1007/978-3-030-66504-3_13
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