Multimedia Security And Forensics

Multimedia Security And Forensics
Authentication of Digital Images
CS525 Semester Project
Spring 2006
Sarah Summers
Sarah Wahl
Seeing is believing … or is it?
Easy to be deceived
 Identify image tampering methods.
 Assess methods available for protecting
 Assess image authentication techniques.
 Identify directions for future work.
Categories of Image Tampering
 There are three main categories of image
 Enhancing
 Compositing
 Copy/Move
 Changing the color
of objects
 Changing the
weather conditions
 Blurring out objects
Combining two or
more images to
create a new image
Copying regions of
the original image
and pasting into
other areas.
The yellow area has
been copied and
moved to conceal
the truck.
What can be done to protect
digital images?
 Fragile watermarks
 Semi-fragile watermarks
 Self-embedding watermarks
 Digital cameras with watermarking
Digital Fingerprinting/Signatures
 Digital cameras with fingerprinting
Digital Watermarking
 The basic concept of digital watermarking
an image is that a low level signal is
placed directly into the image data.
 Any manipulation of the image will impact
the watermark and subsequent retrieval of
the watermark and examination of its
condition will indicate if tampering has
Fragile Watermarks
 Fragile watermarks are designed to detect
every possible change in pixel values .
 Variety of Techniques but in most cases,
the watermark is embedded in the least
significant bit (LSB) of the image.
 Advantages: Pick up all image
manipulations – malicious and nonmalicious
 Disadvantages: Too sensitive
Semi-Fragile Watermarks
 They are robust, to a certain extent, and are less
sensitive to pixel modifications.
 Techniques:
 Divide image into blocks and utilize bits from each
block to calculate a spread spectrum noise like signal
which is combined with DCT coefficients and inserted
as a watermark.
 Divide image into blocks, construct watermark in DCT
domain from pseudo-random zero-mean unit variance
Gaussian numbers, take the inverse DCT and insert
into the image.
 Advantage: less sensitive than fragile watermarks
 Tampered images result in lost information. The previous
techniques will only detect and localize areas of interest
when authentication is carried out.
 Self-embedding allows tamper detection and recovery of
missing information.
 General concept is that the image is embedded in itself
in an encrypted form.
 Advantage: Potential for original data to be retrieved.
 Disadvantage: Tampering with the image can remove
blocks of the original image making retrieval of content
Digital Cameras with
Watermarking Capabilities
 Watermarking based on secret key, block ID and
content. The image is divided into blocks and
each block watermarked using a frequency
based spread spectrum technique incorporating
the secret key, block ID and block content.
 Image of photographers iris is combined with the
camera ID, the hash of the original image and
other details specific to the camera.
Digital Fingerprints/Signatures
 Based on the concept of public key
 Hashed version of image is encrypted
using a private key.
 Encrypted file provides a unique
signature/fingerprint of the image which
can be used to authenticate by decryption
with public key.
 Mainly used in transmission of images.
Digital Cameras with
Fingerprinting Capabilities
 Epson Image Authentication System (IAS)
 The IAS software in the camera instantly
seals the captured images with an
invisible digital fingerprint.
 Verification of image is achieved by any
PC with Image Authentication System
software installed
Authentication Techniques
 Active Authentication
 Rely on the presence of a watermark or
 Require knowledge original image
 Algorithm/key used to embed the watermark
or fingerprint.
 Passive Authentication
 No requirement of knowledge of original
 Does not rely of presence of watermark or
Passive Authentication
 Detecting Copy-Move
 Detecting Traces of Re-sampling
 Detecting Light Inconsistencies
Copy-Move Detection
Original Image
Exact Match
Tampered Image
Robust Match
Copy-Move Detection
Original Image
Tampered Image
PCA Detection
Re-sampling Detection
Original Image
Fourier Transform of
unaltered region
Tampered Image
Periodic pattern in Fourier
Transform of altered region
Inconsistencies in Lighting
Tampered Image
Genuine Image
Future Research
 Development of a better self embedding
 Development of an all inclusive passive
authentication technique.
 Digital image forgeries can be used to
deceive the public and the authorities.
 They are here to stay.
 Until non destructible/ non removal digital
watermarks are perfected, passive
authentication will remain necessary.
 Currently no single passive authentication
technique can detect all types of digital
Hany Farid, Creating and Detecting Doctored and Virtual Images: Implications to The
Child Pornography Prevention Act, Technical Report, TR2004-518, Dartmouth
College, Computer Science.
Detection of Copy-Move Forgery in Digital Images, Jessica Fridrich, David Soukal
and Jan Lukas, Proceedings of Digital Forensic Research Workshop, August 2003,
Detection of image alterations using semi-fragile watermarks, E.T. Lin, C. I.
Podilchuk, and E.J. Delp,
Semi-fragile watermarking for Telltale Tamper Proofing and Authenticating, H. H. Ko
and S. J. Park,
Methods for Tamper Detection in Digital Images, Jiri Fridrich, Proc. ACM Workshop
on Multimedia and Security, Orlando, FL, October 30-31, 1999, pp. 19-23,
Information Authentication for a Slippery New Age, S. Walton, Dr. Dobbs Journal, Vol.
20, No. 4, pp 18-26, Apr 1995
Blind Detection of Photomontage using Higher Order Statistics, T. Ng, S. Chang and
Q. Sun,
References (continued)
A Digital Watermark, R. van Schyndel, A. Tirkel and C. Osborne
, Proceedings of
the IEEE International Conference on Image Processing, vol. 2, pp. 86-90, Austin,
Texas, November 1994
A Watermark for Image Integrity and Ownership Verification, P. Wong, IS&T’s 1998
Image Processing, Image Quality, Image Capture, Systems Conference, Portland,
Oregon, May 1998, pp. 374 – 379
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Proc. ICIP’97, Santa Barbara, California 1997
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Methods for Detecting Changes in Digital Images, J. Fridrich, Proc. of The 6th IEEE
International Workshop on Intelligent Signal Processing and Communication Systems
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A Robust Content Based Digital Signature for Image Authentication, M. Schneider
and S. Chang, Proceedings of the International Conference on Image Processing,
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References (continued)
A New Fingerprinting Method for Digital Images, V. Fotopoulos and A. N. Skodras,
Mehdi Kharrazi, Husrev T. Sencar and Nasir Memon, Blind Source Camera
Identification, International Conference on Image Processing, 2004, ICIP’04, Volume
1, 24-27 Oct. 2004, pp. 709 -712
Rotation, Scale and Translation Invariant Digital Image Watermarking, J.J.K.
O’Ruanaidh and T. Pun, Proceedings of the ICIP, VOl. 1, pp 536-539, Santa Barbara,
California, Oct 1997.
Secure Digital Camera, Paul Blythe and Jessica Fridrich,
Alin C. Popescu and Hany Farid, Exposing Digital Forgeries in Color Filter Array
Interpolated Images, IEEE Transactions on Signal Processing, Vol. 53, Issue 10, Part
2, October 2005, pp 3948-3959
Epson's Image Authentication for digicams,
When is Seeing Believing, W. J. Mitchell, Scientific American, pp. 44 -49, February
References (continued)
Exposing digital forgeries by detecting inconsistencies in lighting by M. K. Johnson
and H. Farid, ACM Multimedia and Security Workshop, New York, NY, 2005,
Exposing Digital Forgeries by Detecting Traces of Re-sampling, A. C. Popescu and H.
Farid, IEEE Transactions on Signal Processing, 53(2):758-767, 2005,
Exposing digital forgeries by detecting duplicated image regions, A. C. Popescu and
H. Farid, Technical Report 2004-515, Dartmouth College,
A Tutorial on Principal Components Analaysis, Lindsay I. Smith
Automatic Estimation of the Projected Light Source Direction, P. Nillius and j. –O.
Eklundh, Proceddings of the IEEE Computer Science Conference on Computer
Vision and Pattern Recognition, 2001
Protection of Digital Images Using Self Embedding, J. Fridrich and M. Goljan,
Symposium on Content Security and Data Hiding in Digital Media, New Jersey
Institute of Technology, May 14, 1999,
A Model for Image Splicing, T. Ng and S. Chang, ICIP '04. International Conference
on Image Processing,. Volume 2, 24-27 Oct. 2004 Page(s):1169 - 1172 Vol.2