Detection of image alterations using semi fragile watermarks
This presentation is the property of its rightful owner.
Sponsored Links
1 / 28

Detection of Image Alterations Using Semi-fragile Watermarks PowerPoint PPT Presentation


  • 70 Views
  • Uploaded on
  • Presentation posted in: General

Detection of Image Alterations Using Semi-fragile Watermarks. Eugene T. Lin † , Christine I. Podilchuk ‡ and Edward J. Delp †. † Purdue University School of Electrical and Computer Engineering Video and Image Processing Laboratory ( VIPER) West Lafayette, Indiana

Download Presentation

Detection of Image Alterations Using Semi-fragile Watermarks

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript


Detection of image alterations using semi fragile watermarks

Detection of Image Alterations Using Semi-fragile Watermarks

Eugene T. Lin†, Christine I. Podilchuk‡ and Edward J. Delp†

†Purdue University

School of Electrical and Computer Engineering

Video and Image Processing Laboratory (VIPER)

West Lafayette, Indiana

‡Bell Laboratories, Lucent Technologies

Murray Hill, New Jersey


Overview

Overview

  • Introduction

    • Image authentication

    • Fragile watermarks

    • Robust watermarks

    • Semi-fragile watermarks

  • Description of proposed technique

  • Results

  • Conclusion


Image authentication

Image Authentication

  • Identify the source of an image

  • Determine if the image has been altered

  • If so, locate regions where alterations have occurred

  • Authentication watermark

    • watermark is imperceptible under normal observation

    • allows user to determine if image has been altered after mark embedding


Fragile watermarks

Fragile Watermarks

  • Watermark is rendered undetectable after slightest modifications to marked content

  • Typically able to localize alterations with high degree of precision

  • Sensitivity achieved through use of hash functions

  • Problem: if lossy compression is applied to marked image, mark is destroyed even though compressed image remains perceptually similar


Robust watermarks

Robust Watermarks

  • Resists removal attempts

  • Examines large regions of image, limited localization of alterations

  • Robustness typically achieved through spread-spectrum techniques

  • Problem: robust watermark may remain even after alterations that change the visual message conveyed by the image


Semi fragile watermarks

Semi-Fragile Watermarks

  • Able to detect and localize significant “information altering” transformations (feature replacement)

  • Able to tolerate some degree of “information preserving” transformations (lossy compression)

  • Suitable in authentication applications where legitimate use includes lossy compression or other image adjustment by users


Semi fragile watermarks1

Semi-Fragile Watermarks

  • Challenges for fragile watermark  semi-fragile watermark:

    • LSB plane embedding not tolerant to compression

    • Cryptographic hash functions too sensitive

  • Challenges for robust watermark  semi-fragile watermark:

    • Reduce region size used in mark detection but retain enough SNR to achieve reliable detection

    • Boundary effects


Description of proposed technique

Description of Proposed Technique

  • Watermark construction

    • DCT construction, spatial embedding

  • Watermark detection

    • Based on differences of adjacent pixel values

    • Most natural images contain large regions of relatively smooth features


Watermark construction

Watermark Construction

DCT Watermark Generation


Watermark construction1

DCT watermark Generation

IDCT

W

X

Marked Image

Original Image

+

Y=X+W

Watermark Construction

  • After watermark is constructed in DCT domain, it is transformed to spatial domain and embedded


Watermark detection

Watermark Detection

  • Independent detection performed on each block, for localizing altered blocks

  • Define two operators:


Example of differential operators

Example of Differential Operators


Watermark detection1

Watermark Detection

  • Tb = Block of image being tested

  • Wb = Corresponding block of watermark image

  • Detector uses both row and column differences:


Block test statistic

Block Test Statistic

  • Tb* and Wb* are correlated to compute block test statistic b:

b T:Block is likely authentic

b < T:Block is likely altered.


Results gradient

Results - Gradient

Original “Gradient”

Altered “Gradient”

Total Blocks: 682, Altered:300 (44%)

Detector Block size:16x16, embedding =5.0


Results gradient1

Results - Gradient


Results gradient2

Results - Gradient


Results sign

Results - Sign

Original “Sign”

Altered “Sign”

Total Blocks: 1536, Altered:77 (5%)

Detector Block size:16x16, embedding =5.0


Results sign1

Results - Sign


Results sign2

Results - Sign


Results money

Results - Money

Original “Money”

Altered “Money”

Total Blocks: 570, Altered:143 (25%)

Detector Block size:16x16, embedding =5.0


Results money1

Results - Money


Results money2

Results - Money


Results girls

Results - Girls

 Original “Girls”

Altered “Girls” 

Total Blocks: 5704, Altered:951 (17%)

Detector Block size:16x16, embedding =5.0


Results girls1

Results - Girls


Results girls2

Results - Girls


Detection of image alterations using semi fragile watermarks

Detection Performance

Embed: =5.0

Detection:

T=0.1

blocksize=16x16

JPEG-60

bitrate=0.90 bpp

93% correct detection

4% false positive

17% misses


Conclusions

Conclusions

  • A semi-fragile watermarking technique was proposed which classifies about 70%of blocks correctly for moderate JPEG compression, 90% for light JPEG compression

  • Detector has problems with edges and textures

  • Future work:

    • Integrate a visual model to embed mark at higher strengths in textured areas


  • Login