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Analysis of Denoising Filters for PRNU Noise Extraction in Camera Identification

Explore the efficacy of denoising filters in extracting photo response non-uniformity noise for source camera identification. Compare Mihçak and Argenti filters through experimental results and discuss future trends in multimedia forensics.

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Analysis of Denoising Filters for PRNU Noise Extraction in Camera Identification

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  1. 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

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

  3. 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 • Whatmodel? • Specific device? BRAND MODEL D40x Nikon L12 Canon D50 Sony S650 etc…

  4. Digital Camera Acquisition Process [Fridrich06] • Fingerprint from the acquisition process • CCD sensor imperfections

  5. 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 Fingerprint unique for each sensor

  6. Digital Camera Model noisy image noise free image PRNU Additive-multiplicativerelation Find , F denoising filter

  7. Digital Camera Identification fingerprint estimation taken by the same camera A camera A PRNU

  8. Digital Camera Identification fingerprint detection The test image imm(k) is taken by camera A? camera A imm(k) is taken by camera A

  9. 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 • Fingerprint estimation from N images (no smooth images)‏ • Fingerprint detection: correlation; given an image we calculate the noise pattern and then correlated with the known reference pattern from a set of cameras. • Decision: threshold, Neymann Pearson criterion FAR=10^-3 [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.

  10. Mihcak’s Filter • 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 LL subband For each detail subband Coeff.

  11. Argenti’s Filter • signal-dependent noise model • The parameters to be estimated are: • and • On homogeneous pixels, log scatter plot regression line and thenMMSE filter in spatial domain. • MMSE (minimum mean-square error)filter in undecimated wavelet domain estimate noise free image noisy image stationary zero-mean uncorrelated random process electronics noise (AWGN) For each detail subband LL subband Noise estimate Iterative estimate

  12. 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. Mihçak filter: 99.09% Argenti filter: 96.61% Low Pass filter: 84.44%

  13. Results- denoising filter comparison • Calculate a threshold that minimize the FRRwith 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. Mihçak filter: 99.09% Argenti filter: 96.61% Low Pass filter: 84.44%

  14. Results- denoising filter comparison • Correlation values for 20 images from a Olympus FE120 with 5 fingerprints. LP filter Mihcak filter Argenti filter Mihçak filter: 99.09% Argenti filter: 96.61% Low Pass filter: 84.44% LP filter Mihcak filter Argenti 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.

  15. 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.

  16. Thank you

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