Mean quantization based image watermarking Source: Image and Vision Computing, vol. 21, no. 8, Aug. 2003, pp. 717-727. Authors: L.-H. Chen and J.-J. Lin Speaker: Wei-Liang Tai

Outline Introduction to Watermarking Introduction to Wavelet Transforms The Proposed Method Experimental Results Conclusions

Introduction - Watermarking Visible Invisible

Introduction - Watermarking (Cont.) Invisible Watermarking Robust Watermarking It can against image processing such as filtering, lossy compression, sharpening, and so on. Fragile Watermarking It can detect any tiny alteration to the pixel value. Semi-fragile Watermarking It is moderately robust to lossy compression such as JPEG.

Introduction – Wavelet Transforms Haar Wavelet Transform Phase 1) Horizontal : ㄅ ㄆ ㄇ ㄈ ㄉ ㄊ ㄋ ㄌ ㄍ ㄎ ㄏ ㄐ ㄑ ㄒ ㄓ ㄔ A B C D E F G H I J K L M N O P A+B C+D A-B C-D E+F G+H E-F G-H I+J K+L I-J K-L M+N O+P M-N O-P Phase 2) Vertical : ㄅ ㄆ ㄇ ㄈ ㄉ ㄊ ㄋ ㄌ ㄍ ㄎ ㄏ ㄐ ㄑ ㄒ ㄓ ㄔ ㄅ+ㄉ ㄆ+ㄊ ㄇ+ㄋ ㄈ+ㄌ ㄍ+ㄑ ㄎ+ㄒ ㄏ+ㄓ ㄐ+ㄔ ㄅ-ㄉ ㄆ-ㄊ ㄇ-ㄋ ㄈ-ㄌ ㄍ-ㄑ ㄎ-ㄒ ㄏ-ㄓ ㄐ-ㄔ

Introduction – Wavelet Transforms (Cont.) JND (Just Noticeable Difference) Range = [ T-JND, T+JND ]

The Proposed Method Watermark W=w1w2…wL Embed in subbands LL1 and HH1 N where is the number of coefficients used to encode a watermark bit ex: N=3, w1=0 Normalize, and compute mean value HH1 JND 6 7 9 4 6 7 4 6 7 2 3 -2 -4 16 10 20 25 18 26 16 10 20 Extract Inverse - normalize

Experimental Results Original image Watermarked image

Experimental Results (Cont.)

Conclusions Exploiting human visual system (HVS) Robust to common attacks