In a past post, we presented several improvements we did to Amped Authenticate’s Camera Identification filter based on PRNU analysis. Those improvements propagated also to the PRNU Tampering filter so that Amped Authenticate also features an improved algorithm for forgery localization. Improvements mainly include:
Peak-to-Correlation Energy-based (PCE) analysis
During block-based analysis, the PCE is computed and the point yielding the maximum PCE value (peak) is considered. If the peak is in the expected position, the block is considered authentic; if the peak is in a different position, then the PCE value is compared with a threshold to decide the authenticity of the block.
Support for multi-core processing
If your CPU features multiple logical cores, block-based analysis will run in parallel, thus reducing the computation time.
Faster, easier training
Thanks to PCE robustness, there is no need to train a separate model for forgery detection: you can use the same .crp file created for Camera Identification.
Forgery localization for cropped images
If the image is cropped and/or rotated before or after manipulation, the PRNU Tampering filter will detect cropping, compensate for it and run the forgery localization algorithm. The same applies to resizing and/or rotation. Combination of resizing and cropping is not supported yet.
Alert for unreliable regions
PRNU-based forgery localization is not reliable in saturated areas (i.e., totally white or black regions of the image). Indeed, for those pixels, it is impossible to discriminate between the image content and the actual sensor noise. The new version of PRNU-based forgery localization enables highlighting of white and black saturated pixels (marked in yellow and blue, respectively), in order to help the analyst rule out false alarms. Continue reading