Analysis of denoising filters for photo response non uniformity noise extraction in

source camera identification

Irene Amerini, Roberto Caldelli, Vito Cappellini, Francesco Picchioni, Alessandro Piva

irene.amerini@unifi.it

Santorini,06.07.09

Outline

• Multimedia Forensics• Source Camera Identification• Digital camera acquisition process• Analysis of different wavelet denoising filters• Experimental results• Future Trends

Multimedia Forensics

The goals of multimedia forensics are:• Forgery detection • Source Identification: determine the device that acquired an image (scanner, CG, digital camera, ...)

Source Camera Identification Which camera brand took this picture What model? Specific device?

Nikon

Canon

Sony

etc…

BRAND

D40x

L12

MODEL

D50

S650

Digital Camera Acquisition Process

[Fridrich06]

Fingerprint from the acquisition process

• CCD sensor imperfections

Sensor Imperfections• defective pixels: hot/dead pixels (removed by post-processing)• shot noise (random)

• pattern noise (systematic)Fixed Pattern Noise: dark current (exposure, temperature) suppressed by subtracting a dark frame from the image.

Photo Response Non Uniformity: caused by imperfection in manufacturing process

• slightly varying pixel dimensions• inhomogeneities in silicon wafer.

PRNU as Fingerprintunique for each sensor

Digital Camera Model

0 0I I I K θ

Additive-multiplicative relation

Find , F denoising filter(I)IK F

0II

noisy image

noise free image

PRNUK

K

camera A

Digital Camera Identificationfingerprint estimation

taken by the same camera A

PRNU

Digital Camera Identification fingerprint detection

The test image imm(k) is taken by camera A?

imm(k) is taken by camera A

camera A

Digital Camera Identification denoising filter

The digital filter has an important role for PRNU extraction!

Comparison and analysis of two denoising filters:

Previously used Mihçak Filter [1]additive noise model

Novel Argenti-Alparone Filter [2]signal-dependent noise model

[1] K. Ramchandran M. K. Mihcak, I. Kozintsev, “Spatially adaptive statistical model of wavelet image coefficients and its application to denoising”, 1999.[2] L. Alparone F. Argenti, G. Torricelli, “Mmse filtering of generalised signal-dependent noise in spatial and shift-invariant wavelet domain“, 2005.

WUIII 0 0

0 0I I I K θ

• additive noise model (AWGN)

•spatially adaptive statistical modelling of wavelet coefficients

• 4 level DWT (Daubechies)

• MAP (Maximum A Posteriori) approach to calculate the estimate of the signal variance

• Wiener filter in the wavelet domain

Mihcak’s Filter

(k)(k)(k) nXG

Coeff.

LL subband

)(ˆ kX

For each detail subband

2̂

• signal-dependent noise model

• The parameters to be estimated are: and On homogeneous pixels, log scatter plot regression line and then MMSE filter in spatial domain.

• MMSE (minimum mean-square error) filter in undecimated wavelet domain

noise free image noisy image stationary zero-mean uncorrelated random process electronics noise (AWGN)

Argenti’s Filter

LL subband

For each detail subband

estimate

Iterative estimate

U2

αI

Noise estimate

0IU

W

WUIII 0 0

Results- denoising filter comparison

• 10 digital cameras.

• Data set:• training-set to calculate the fingerprint: 40 images for each camera.• test-set: 250 images for each camera.

• A low pass filter (DWT detail coefficients are set to zero) is used to provide a performance lower bound.

Results- denoising filter comparison

• Calculate a threshold that minimize the FRR with Neymann-Pearson criterion with a priori FAR=10^-3.

• Argenti’s filter has a significative lower FRR for Samsung and Olympus.• In the general the two filters show a comparable behavior.

Argenti filterMihcak filterLP filter

•The higher values are those related to the correlation between the noise residual of the Olympus FE120 images and its fingerprint.

• The distributions of the correlation values are well separated in the Argenti cases.

• Correlation values for 20 images from a Olympus FE120 with 5 fingerprints.

Results- denoising filter comparison

LP filter Mihcak filter Argenti filter

Conclusions

• Introducing a novel filter for the estimation of PRNU.

• An analysis on different kinds of denoising filters for PRNU extraction as been presented.

• Experimental results on camera identification have been provided.

Future Trends• Improve methodology extraction for PRNU.• Force parameter in the Argenti noise model and repeat the experiments.

1

Thank you