Detection of image alterations using semi fragile watermarks
1 / 28

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

  • 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

I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.

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



  • 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




Marked Image

Original Image



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





bitrate=0.90 bpp

93% correct detection

4% false positive

17% misses



  • 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