Forensic Imaging The History of Image Forgery Image Splicing

							   Forensic Imaging
The History of Image Forgery
      Image Splicing


         Yaniv Lefel
         Hagay Pollak
Cloning …
History
=



+
Is this
real ?



Look at     Caribbean   Virginia
            2005        2004
the
shadows
…


Watch the
lightning
…
Fake or real ?




                 link
                    Why?
• Fake images are everywhere due to
  – Popularity of digital cameras.
  – Availability of desktop imaging software
    allows easy manipulation of images.
          Image authenticity
• A fake image can be defined as an image of
  an object or scene that wasn't captured as
  the image would imply.
• The fake images that concern us most are
  those advertised as real.
        Understanding faking
• Imaging properties are far too complex for
  manually creating one - one pixel at a time.
• Complex calculations are required in order
  to take into account the image physical
  properties.
• High quality software isn’t accessible to the
  average PC user.
   Understanding faking (cont’)
• Common method of faking is by editing an
  existing image that was captured by a
  camera.
      Origin of editing pictures
• 19th century - remove wrinkles and
  blemishes.
• Dark room tricks - adding and removing
  people from images (being unable to get an
  entire family together for a family portrait).
Family portrait example
 Change of context – Example 1
• The Surgeon's
  Photo, 1934,
  reportedly
  showing the
  Loch Ness
  Monster
 Change of context – Example 2
• Cottingley
  Hoax, 1917,
  reportedly
  showing
  winged
  fairies
 Embedding an image in another
• All you need is a PC with image editing
  software.
• The software allows the creator to modify
  the image to the appropriate size and
  rotation.
Embedding examples
     Detecting fake images using
           common sense
• Our perception is the first line of defense at
  identifying fake images.
• Example (man holding a cat):
  – The cat is obviously too big.
  – The man should have leaned backwards more
    to properly hold a cat of this weight.
      Detecting fake images using
        common sense (cont’)
• Our perception can fail to
  detect a fake image if there is
  no cause for suspicion.

• TV Guide used Ann-
  Margret's body for a picture
  of Oprah Winfrey.
Realistic computer generated images
• Fake: Columbia
  disaster taken
  from a satellite
• Real: A
  computer
  generated image
  from the movie
  Armageddon.
          Detecting fake images




An image has an encoded watermark that contains the
edge information of original image, revealing an alteration
that has been made to the original image.
The gray-level histogram may show signs that the image
has been altered.
Inconsistent noise properties may be apparent in altered
images.
Measuring the vanishing points
reveals that a window has been
added to this building.
Perspectives lines converge to a
single point.
       Splicing and Blending
• Composing multiple images into a single
  image.
• Input:
  – Multiple images.
• Output
  – Single composed (e.g. Panoramic) image.
    Splicing and Blending (cont)
• Splicing <-> composing
  – At the end all the images are composed into a single
    image so that the final image appears to show no traces
    of the composition.
• The Challenge:
  – While taking the pictures (images) the scene might
    change – e.g: People moving around, cloud shadows
    move.
  – The images are not always identical in the areas the
    images overlap.
    Splicing and Blending (cont)
• Idea:
  – The technique extracts out parts of each of the
    individual images to construct the composed
    image (panorama).
  – The final result is not really a true wide angle
    snapshot of the busy scene, but it looks like it
    could have been.
Splicing and Blending (cont)
          Splicing and Blending
               Image feathering
• Idea:
  – Combine two images into one, by averaging the
    color values from the two images.
• Problem:
  – When the scenes in the two images are different
    the result may contain an effect called
    “Ghosting” where an object appears blurred in
    the result image.
          Splicing and Blending
           Composing using “snakes”
• Idea:
  – This splice technique computes a curved line
    from top to bottom in the common region , and
    assembles the composite by taking the part of
    the first image to the left of the line and places
    it adjacent to the part of the second image to the
    right of the line.
                                Splicing and Blending
                                       Composing using “snakes”




               http://torina.fe.uni-lj.si/~tomo/ac/Snakes.cgi

              http://www.ecs.soton.ac.uk/~msn/book/new_demo/Snakes/


http://www.markschulze.net/snakes/index.html
                     Snakes
• Define a set of points as a curved line – the snake.
  These points move to the lowest “energy” point in
  the local neighborhood, defined by the "Energy
  Function". This continues until the snakes stop
  moving.
• This establish the problem as the minimization of
  some cost function.
• Use established optimization techniques to find
  the optimal (minimum cost) solution.
        Splicing and Blending
     Creating tileable image texture tiles

• The same technique can be used on a single
  image, to create a synthetically generated
  tiled larger image.
  – By cutting the single image in the middle,
    splicing the left and right parts (see next slide)
    into a new tile.
  – Repeat the process twice vertically and
    horizontally.
  – Then tiling the new generated tile.
Splicing and Blending
 Composing using “snakes”
                    What now ?
Law and Order                  Journalism
(proving authenticity)



                Scientific
                publications
So, is this real ?
What about this ?




                    link
            Well ?
CG   CG     Real     CG            Real




CG   Real   CG       Real          Real

                            link
This Is The End My Friend