Hsu
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Introduction
Background
-CFA Interpolation and why necessary -Effect of tampering on CFA interpolation
Methods
-EM algorithm to generate probability -Threshold for detection -Normalization of data for automation
Results
Conclusions
- Buggy areas: places with large areas of pixels with the same pixel so that the probability map shows the same value for a large swath - Automation: normalization between different images - Use information about the CFA to determine tampering (i.e. threshold adjustment done based on CFA interpolation technique - Classification using frequency information between channels instead of processing them differently Could give better estimate in the case of CFA interpolators across multiple channels or get better estimate of frequency across different channels
References - Resources and related work
References
Software