Source: Signal Processing: Image Communication 16 (2001) pp

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Presentation transcript:

Halftone Image Data Hiding with Intensity Selection and Connection Selection Source: Signal Processing: Image Communication 16 (2001) pp. 909-930 Authors: Ming Sun Fu and Oscar C. Au Speaker: Hsien-Chu Wu Date: Nov. 8, 2001

Outline Introduction Previous works -Data hiding self toggling (DHST) -Data hiding pair toggling (DHPT) -Data hiding smart pair toggling (DHSPT) The proposed intensity selection algorithm (IS) The proposed connection selection algorithm (CS) Experimental results Conclusions

Introduction Halftone image (binary image) -a bit/pixel -pixel value: 0,1 Gray-level image -a byte/pixel -pixel value: 0-255 X 128 white (0) X<128 Black (1)

Introduction---hiding data in a halftone image 1 Host image Changed image 1 1  

Data hiding self toggling (DHST) Host image “salt-and-pepper” artifacts PRNG(SK) … Hidden data:010100110101…

Data hiding pair toggling (DHPT) Master pixel 1 - - -> 0 1 PRNG(SK) Choose one to be the slave pixel 0 - - - > 1

Data hiding smart pair toggling (DHSPT) Master pixel 1 - - -> 0 1 PRNG(SK) Choose one to be the slave pixel by using “connection” 0 - - - > 1

Data hiding smart pair toggling (DHSPT) The weight of the candidate x x1 x2 x3 x4 x5 x6 x7 x8 x9 1 2

Data hiding smart pair toggling (DHSPT) Connection value = 1+2+2+1=6 1 Connection value =0 Connection value =2+2=4 1

The proposed intensity selection (DHSPT-IS) Hide one bit of the embedded data at M pseudo-random locations M-pixel-sum parity: If the hidden bit is not equal to the M-pixel-sum parity, one of the M pixels will be changed.

The proposed connectivity selection (DHSPT-CS) Compute the connection value for each of the M locations and choose the location with the largest connection.

Experimental results Visual quality measure scores:

Experimental results

Experimental results Fig 4. Error image for DHSPT

Experimental results Fig. 9. Error image for DHSPT-IS4

Experimental results Fig. 9. Error image for DHSPT-CS4

Conclusions The visual quality of IS and CS is better for larger M. The proposed IS and CS provide a tradeoff between visual quality and computational complexity.