Iwahashi, A.; Yoshida, N.; Matsumoto, T. Directional Emphasis Filtering in Artifact Metrics for Rejecting Raster-Scanning-Created Clones. Preprints2023, 2023122194. https://doi.org/10.20944/preprints202312.2194.v1
APA Style
Iwahashi, A., Yoshida, N., & Matsumoto, T. (2023). Directional Emphasis Filtering in Artifact Metrics for Rejecting Raster-Scanning-Created Clones. Preprints. https://doi.org/10.20944/preprints202312.2194.v1
Chicago/Turabian Style
Iwahashi, A., Naoki Yoshida and Tsutomu Matsumoto. 2023 "Directional Emphasis Filtering in Artifact Metrics for Rejecting Raster-Scanning-Created Clones" Preprints. https://doi.org/10.20944/preprints202312.2194.v1
Abstract
Artifact metrics is a technology for authenticating artifacts based on their unique characteristics. The artifact-metric system offers "clone resistance," i.e., it makes it highly improbable to create another object exhibiting the same measured values as a genuine artifact. However, determined adversaries may still attempt to create imitations or clones with close physical characteristics to those of registered products, even if they cannot perfectly replicate the genuine product. Such clones serve to deceive users into believing they are genuine rather than counterfeits. Thus, in this study, we consider a scenario in which we measure raster-scanning-generated clones via a non-raster scanning method. Further, we employ image processing techniques to generate image data representing the clones and theoretically assess the filtering effects on these images. Our findings reveal that applying filters to specific frequency components in the spatial frequency domain can effectively highlight differences between raster-scanning-created clones and the corresponding genuine artifacts. Thus, we demonstrate directional emphasis filtering in artifact metrics as an effective approach for rejecting raster-scanning-created clones.
Computer Science and Mathematics, Signal Processing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
