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Digital Signal Processing 2009 - LCI MICC -

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Analysis of denoising filters for photo response non uniformity noise extraction in source camera identification (Irene Amerini, Roberto Caldelli, Vito Cappellini, Francesco Picchioni, Alessandro ...

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) - Santorini, 06.07.09

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    Digital Signal Processing 2009  - LCI MICC - Digital Signal Processing 2009 - LCI MICC - Presentation Transcript

    • 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 [email_address] 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 mode l ? 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 Fingerprint unique for each sensor
    • Digital Camera Model Additive-multiplicative relation Find , F denoising filter noisy image noise free image PRNU
    • camera A Digital Camera Identification fingerprint 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
      • 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.
        • 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 Coeff. LL subband For each detail subband
      • 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 Noise estimate
    • Results- denoising filter comparison Mihçak filter: 99.09% Argenti filter: 96.61% Low Pass filter: 84.44%
      • 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 Mihçak filter: 99.09% Argenti filter: 96.61% Low Pass filter: 84.44%
      • 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 filter Mihcak filter LP filter Mihçak filter: 99.09% Argenti filter: 96.61% Low Pass filter: 84.44%
      • 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.
    • Thank you