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First we talk about scenario.

Next I introduce the Multimedia Forensic and the state of art. In the end we see methods and results.

Finally I talk about the Future Trends.

This is the Outline of the presentation:

First we talk about the scenario where we

Next I introduce the Multimedia Forensic and also we see the state of art of M.F. In the end we see methods that we developed and the results that we obtain applying this.

Finally I talk about the Future Trends.

First I will introduce the scenario process through the use of a digital camera. The second and the third sections will be devoted to the analysis of the principal techniques exploited respectively for identifying the acquisition device of digital images and for assessing the authenticity of digital images. Some experimental results, in particular for source identification, will be reported and conclusions will be provided in the last two sections.

forensic image analysis is the application of image science and domain expertise

to interpret the content of an image or the image itself in legal matters (SWGIT- www.fbi.gov)

Because details about the processing are not always easily available (they are hard-wired or proprietary), generally is needed to use a simplified model that captures various elements of typical in-camera processing:

We can see the sensor output I where I(0) is the sensor output in the absence of noise gamma is the gamma correction factor

Teta Is a complex of independent random noise components. The multiplicative factor K is a zero-mean noise-like signal responsible for PRNU (the sensor fingerprint)

We can use PRNU like Fingerprint because is embedded into every image and is unique for each digital camera;

When the imaging sensor takes a picture of an absolutely evenly lit scene, the resulting digital image will still exhibit small changes in intensity among individual pixels. These errors include sensor’s pixel defects and pattern noise this last has two major components, namely, fixed pattern noise and photo response non-uniformity noise (PRNU). The most important component of PRNU is the pixel non-uniformity (PNU), which is defined as different sensitivity of pixels to light. The PNU is caused by stochastic inhomogenities present in silicon wafers and other imperfections originated during the sensor manufacturing process. Finally the noise component to be estimated and to be used as intrinsic characteristic of the sensor (fingerprint) is the PNU.

Template deterministoco impresso sopra l’immagine

PNU (pixel non uniformity)

Low frequency defects: rifrazione della luce, particelle di polvere

Because details about the processing are not always easily available (they are hard-wired or proprietary), generally is needed to use a simplified model that captures various elements of typical in-camera processing:

We can see the sensor output I where I(0) is the sensor output in the absence of noise gamma is the gamma correction factor

Teta Is a complex of independent random noise components. The multiplicative factor K is a zero-mean noise-like signal responsible for PRNU (the sensor fingerprint)

Argenti specke noise removal (SAR images)

Basato su modello di rumore solo additivo (modello + semplice)

Idea: usare un filtro basato su un modello di rumore + complesso: signal dependent cioè……I=,….

Modello paragonabile a quello del processo di acquisizione di un digital camera: uguale quando alpha=1

Modello + generico e puà essere ridotto al modello del processo di acquisizione

Modello + complesso

To extract the PRNU (fingerprint) we generally used denoising filtering in particulary in our analysis we have compare:

A basic low pass filter, used like lower bound performance

A mihcak Filter

A Argenti-Alparone Filter

All of this are filter based on Wavelet domain and different noise model. The assumption to apply our techniques is to have a camera available or N images taken by the camera

Prima stima di alpha e sigmau: si riduce il carico computazionale

Da test fatti il raffineanto della stima non incide nei risultati nel caso della source identification magari nello speckle ha più senso

The other two filters showed a comparable behaviour: the FRR has the same ored of magnitude

Argenti’s filter has a significative lower FRR for Samsung and Olympus

In the other case does not exhibit a considerable improvement in the results

Because the filter depends on the reliability of the parameters estimation

The LP filter has the worst behaviour as obviously expected.

The other two filters showed a comparable behaviour: the FRR has the same order of magnitude

Argenti’s filter has a significative lower FRR for Samsung and Olympus

In the other case does not exhibit a considerable improvement in the results

Because the filter depends on the reliability of the parameters estimation

included

Overlap

Introdotto un nuovo filtro usato in un’altra area di ricerca

Modello paragonabile a quello del miodello di acquisizione

Impove parametrs estimation of the Argenti filter

Alpha lo calcoliamo dall’immagine che diamo in pasto alla procedure di stima

In particular provare a forzare alpha=1 (estremo dell’intervallo dei valori) in modo da far coincidere i modelli, vedere se I risultati milgiorano

- 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 What model? Specific device? Nikon Canon Sony etc… BRAND D40x L12 MODEL D50 S650
- 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 = + × +0 0I I I Kθ Additive-multiplicative relation Find , F denoising filter(I)IK F−= 0I I noisy image noise free image PRNUK K
- 7. camera A Digital Camera Identification fingerprint estimation taken by the same camera A PRNU
- 8. Digital Camera Identification fingerprint detection The test image imm(k) is taken by camera A? imm(k) is taken by camera A 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 [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 +⋅+= α 0 = + × +0 0I I I Kθ
- 10. • 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 2 ˆσ
- 11. • 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 U 2 σ α I Noise estimate 0I U W WUIII 0 +⋅+= α 0 α α
- 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.
- 13. 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.
- 14. 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
- 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. 1α =
- 16. Thank you

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