1 / 16

LSB Matching Revisited

LSB Matching Revisited. Source: IEEE Signal Processing Letters (Accepted for future publication) Authors: Jarno Mielikainen Speaker: Chia-Chun Wu ( 吳佳駿 ) Date: 2006/03/13. Outline. Introduction LSB replacement The proposed scheme Experimental results Conclusions Comments.

edison
Download Presentation

LSB Matching Revisited

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. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. LSB Matching Revisited Source: IEEE Signal Processing Letters (Accepted for future publication) Authors: Jarno Mielikainen Speaker: Chia-Chun Wu (吳佳駿) Date: 2006/03/13

  2. Outline • Introduction • LSB replacement • The proposed scheme • Experimental results • Conclusions • Comments

  3. Internet Introduction • Steganographic Stego image Cover image Secret message: 01011001110…

  4. LSB replacement xi xi+1 mi mi+1 yi yi+1

  5. Example: f (161, 150) = 0 f (163, 150) = 1 f (162, 150) = 1 f (162, 150) = 1 f (162, 151) = 0 The proposed scheme • Modified version of the LSB method • Binary function f (l, n) Property 1: Property 2:

  6. Embedding algorithm Embedding algorithm for a pair of pixels.

  7. Case 1: embedding “0”, “0” 10100010 10010111 0 0 162 151 yi yi+1 mi mi+1 Case 2: embedding “0”, “1” 162 150 0 1 10100010 10010110 yi yi+1 mi mi+1 Embedding messages (1/3) xi xi+1

  8. Embedding messages (2/3) Case 3: embedding “1”, “0” xi xi+1 mi+1 mi yi yi+1 f (163, 150) = 1 f (161, 150) = 0

  9. Embedding messages (3/3) Case 4: embedding “1”, “1” xi xi+1 mi mi+1 yi yi+1 f (163, 150) = 1 f (161, 150) = 0

  10. Extracting messages xi xi+1

  11. Example - 1

  12. Example - 2

  13. Experimental results (1/2) • 1000 JPEG images • All size 384×256 • HCF COM detectors The center of mass (COM) of the histogram characteristic function (HCF) introduced by Harmsen et al. [4] Ker [5] proposed using the adjacency histogram instead of the usual histogram.

  14. Experimental results (2/2) The x-axis has been scaled to focus on a region of interest. ROC curves for the calibrated adjacency HCF COM. ROC curves for the calibrated HCF COM.

  15. Conclusions • The proposed method allows an embedding of the same amount of information into the stego image as LSB matching but with fewer changes to the cover image. • The detection of the existence of the hidden messages using the HCF COM-based detectors is less efficient against the method compared to LSB matching.

  16. Comments • The embedding cannot be performed for saturated pixels, i.e., pixels that have either a minimal (0) or maximal (255) allowable value.

More Related