E-Forensics

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Digital Forensics MICC LCI

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  • image, video and audio 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)
  • Lens system: concave e convesse per prevenire aberrazione cromatica e sferica oppure lenti asferiche Auto-esposimetro Auto-focus Unità di stabilizzazione Filtri infrarossi; anti-aliasing filter CFA per produrre un’immagine a colori Sensor: matrice di fotodiodi; quando la luce colpisce il sensore ciascun pixel del sensore generano un segnale proprorzionale all’intensità luminosa che è poi convertita in un segnale digitale con un convertitore analogico-digitale DIP Digital Image Processor
  • Crypto: il digest è legato strettamente al contenuto e viene definito un particolare formato e non è possibile usarne altri; per ogni midifca fatta sull’immagine il digest cambia.
  • È stato utilizzato con successo per la compressione delle immagini
  • È stato utilizzato con successo per la compressione delle immagini
  • DFT analysis
  • E-Forensics

    1. 1. Distinguishing between camera and scanned images by means of frequency analysis Roberto Caldelli, Irene Amerini and Francesco Picchioni {roberto.caldelli, irene.amerini, francesco.picchioni}@unifi.it 20 January 2009
    2. 2. Outline <ul><li>Multimedia Forensics </li></ul><ul><li>Camera vs Scanner </li></ul><ul><li>Sensor fingerprint </li></ul><ul><li>Proposed methodology </li></ul><ul><li>Experimental results </li></ul><ul><li>Conclusions </li></ul>
    3. 3. Multimedia Forensics <ul><li>The goal of multimedia forensics is to </li></ul><ul><ul><li>detect image forgeries </li></ul></ul><ul><ul><li>determine the source of an image (scanner, CG, digital camera, ...) </li></ul></ul><ul><ul><li>recover image history </li></ul></ul><ul><li>Acquisition device identification </li></ul><ul><ul><li>Kind of device </li></ul></ul><ul><ul><li>Brand </li></ul></ul><ul><ul><li>Specific device </li></ul></ul><ul><li>Assessing image integrity </li></ul><ul><ul><li>Copy-move </li></ul></ul><ul><ul><li>Splicing </li></ul></ul><ul><ul><li>Double JPEG compression </li></ul></ul>
    4. 4. Camera vs Scanner- Acquisition Process Digital Camera CFA and demosaicking Bidimensional sensor array Flat-bed Scanner Tri-linear color filter array: no demosaicing Mono dimensional sensor array
    5. 5. Sensor fingerprint & PRNU <ul><ul><li>Sensor imperfections </li></ul></ul><ul><ul><ul><li>defective pixels: hot/dead pixels (removed by post-processing) ‏ </li></ul></ul></ul><ul><ul><ul><li>shot noise (random) </li></ul></ul></ul><ul><ul><ul><li>pattern noise (systematic): </li></ul></ul></ul><ul><li>Properties PRNU: </li></ul><ul><li>unique for each sensor </li></ul><ul><li>multiplicative noise </li></ul><ul><ul><ul><ul><li>Fixed Pattern Noise : dark current (exposure, temperature) suppressed by subtracting a dark frame from the image. </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Photo Response Non-Uniformity : inhomogenities over the silicon wafer and imperfections generated during sensor manufacturing process (flat fielding) </li></ul></ul></ul></ul>
    6. 6. M N scanning direction PRNU characterization (1/2) De-noised image-DWT Because of the scanner sensor, it is expected that: All the rows are equal, at least ideally!
    7. 7. This is true only ideally, being S corrupted its periodical structure is altered, but most of the energy is located in such spikes!! PRNU characterization (2/2) Consequently, the spectrum of the periodical signal S will be made by spikes equispaced of (NxM)/M=N. By concatenating all the rows in a single signal S composed by NxM samples: Periodical of period M and contains N repetitions
    8. 8. Proposed methodology (1/3) <ul><li>To improve the possible presence of 1-D PRNU, the noise image R is divided in non-overlapping stripes whose height is L : </li></ul><ul><li>horizontally and vertically to investigate both scanning directions </li></ul><ul><li>rows (columns) in a stripe are averaged </li></ul>M L M N M Ideal Bar Code This is done horizontally obtaining Rr and vertically obtaining Rc N/L
    9. 9. Proposed methodology (2/3) Digital Camera Scanner (scanning direction row) Ideal M N/L Then, as explained before, two mono-dimensional signals S r and S c are constructed by tailing all the rows and all the columns.
    10. 10. Proposed methodology (3/3) <ul><li>DFT (Discrete Fourier Transform) is applied to signals S r and S c and the magnitude of the coefficients is computed. </li></ul><ul><li>Samples located in the expected periodical positions and with an amplitude above a defined threshold T are taken. </li></ul><ul><li>Two energy factors F r and F c are then calculated by adding all the DFT coefficients satisfying the previous criterion and the RATIO = F r /F c is evaluated: </li></ul><ul><ul><li>High value : image scanned in a row direction </li></ul></ul><ul><ul><li>Small value : image scanned in a column direction </li></ul></ul><ul><ul><li>Around 1 : image coming from a digital camera (neither energy factor are predominant) </li></ul></ul>
    11. 11. Experimental Results (1/4) <ul><li>4 scanners : </li></ul><ul><li>Epson Expression XL 10000; HP Scanjet 8300, HP Deskjet F4180, Brother DCP 7010 </li></ul><ul><li>7 commercial cameras : </li></ul><ul><li>Canon DIGITAL IXUS i ZOOM, Nikon COOLPIX L12, Fuji Finepix F10, HP Photosmart C935, Nikon D80, Samsung VP-MS11, Sony DSC-P200 </li></ul><ul><li>2000 images, JPEG, TIFF </li></ul><ul><li>Image patch 1024x768 </li></ul>Camera vs Scanner Scanning direction
    12. 12. Experimental Result (2/4) <ul><li>Energy RATIO for 200 scanned images and 200 cameras </li></ul><ul><li>Clustering, no information on scanning direction </li></ul>RATIO >1 inverse taken Digital camera Scanner
    13. 13. Experimental Results (3/4) <ul><li>Energy RATIO for 950 scanned images </li></ul>Column scanning direction row scanning direction
    14. 14. Experimental Results (4/4) <ul><li>Statistical distribution of RATIO for 1000 cameras images and for 1000 scanner images </li></ul>Scanner Camera Ratio Ratio Bin Count
    15. 15. Conclusions <ul><li>A novel technique, based on a DFT analysis, to distinguish between digital camera and scanned images has been presented. </li></ul><ul><li>Scanning direction can be detected too. </li></ul><ul><li>Future Trends </li></ul><ul><li>To establish a statistical threshold T </li></ul>
    16. 16. Distinguishing between camera and scanned images by means of frequency analysis Roberto Caldelli, Irene Amerini and Francesco Picchioni {roberto.caldelli, irene.amerini, francesco.picchioni}@unifi.it 20 January 2009

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