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NCC (Normalised Cross-Correlation)

Definition

The standard measure of similarity between a query noise residual and a reference PRNU pattern. It produces a value between -1 and +1, where 1 indicates perfect agreement. In camera attribution a threshold is set above which attribution is accepted.

Full name
Normalised Cross-Correlation
Value range
-1 to +1
Perfect match value
1
Used in
PRNU-based camera source attribution

Common questions

Why does camera attribution use a threshold rather than requiring an exact match?+

Sensor noise and image processing introduce variability, so an identical, noise-free correlation is never realistically achieved even for the true source camera. An examiner sets a threshold above which the correlation is high enough to conclude the image came from that sensor, balancing false matches against missed true matches.

What can lower an NCC score even for the correct source camera?+

Heavy image compression, resizing, cropping, or additional processing after capture can degrade the noise pattern and pull the correlation value down, which is why examiners consider image processing history alongside the raw NCC score rather than reading the number in isolation.

Related terms

BM3D (Block-Matching 3D)
A state-of-the-art denoising algorithm that groups similar image patches across the image, transforms them jointly, thresholds in the transform domain, and inverts....
Hypothesis Testing in Camera Attribution
The framework in which H0 is 'the query image was not taken by this camera' and H1 is 'it was'. The PCE...
Noise Residual (W_hat)
The per-image estimate of PRNU, computed by subtracting a denoised version of the image from the original. It contains PRNU plus random...
PCE (Peak-to-Correlation Energy)
A refined statistic that divides the peak of the cross-correlation surface by the energy of the non-peak region. PCE is more sensitive...
Wavelet Denoiser
A denoising filter that works in the wavelet transform domain, thresholding coefficients at each scale and direction to remove random noise while...

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