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 Introduction Image authentication Fragile watermarks Robust watermarks Semi-fragile watermarks Description of proposed technique Results Conclusion

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 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 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 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 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 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 DCT Watermark Generation

Watermark Construction After watermark is constructed in DCT domain, it is transformed to spatial domain and embedded DCT watermark Generation IDCT Original Image + Marked Image W X Y=X+W

Watermark Detection Independent detection performed on each block, for localizing altered blocks Define two operators:

Example of Differential Operators

Watermark Detection Tb = Block of image being tested Wb = Corresponding block of watermark image Detector uses both row and column differences:

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 Original “Gradient” Altered “Gradient” Total Blocks: 682, Altered:300 (44%) Detector Block size:16x16, embedding =5.0

Results - Gradient

Results - Gradient

Results - Sign Original “Sign” Altered “Sign” Total Blocks: 1536, Altered:77 (5%) Detector Block size:16x16, embedding =5.0

Results - Sign

Results - Sign

Results - Money Original “Money” Altered “Money” Total Blocks: 570, Altered:143 (25%) Detector Block size:16x16, embedding =5.0

Results - Money

Results - Money

Results - Girls  Original “Girls” Altered “Girls”  Total Blocks: 5704, Altered:951 (17%) Detector Block size:16x16, embedding =5.0

Results - Girls

Results - Girls

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 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