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Digital Image Watermarking. Er-Hsien Fu EE381K-15280 Student Presentation. Overview. Introduction Background Watermark Properties Embedding Detection The Project Introduction Embedding Detection Conclusions. Introduction .

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Digital image watermarking l.jpg

Digital Image Watermarking

Er-Hsien Fu

EE381K-15280

Student Presentation


Overview l.jpg
Overview

  • Introduction

  • Background

    Watermark Properties

    Embedding

    Detection

  • The Project

    Introduction

    Embedding

    Detection

  • Conclusions


Introduction l.jpg
Introduction

  • Watermark--an invisible signature embedded inside an image to show authenticity or proof of ownership

  • Discourage unauthorized copying and distribution of images over the internet

  • Ensure a digital picture has not been altered

  • Software can be used to search for a specific watermark


Background watermark properties l.jpg
BackgroundWatermark Properties

  • Watermark should appear random, noise-like sequence

  • Appear Undetectable

  • Good Correlation Properties

    High correlation with signals similar to watermark

    Low correlation with other watermarks or random noise

  • Common sequences

    A) Normal distribution

    B) m-sequences

W=[1 0 0 1 0

0 1 1 0 1

1 1 0 1 0

0 1 1 1 1

0 1 0 0 0]


Project introduction l.jpg
Project: Introduction

  • Possible for watermark to be binary sequence

  • Error-correction coding techniques

  • Use convolutional codes

  • Decode by Viterbi algorithm

  • Compare with non-coding method

  • See if it improves watermark detection

  • More or less robust to attacks?

  • Additive noise, JPEG Compression, Rescale,

  • Unzign

  • Performance assessed by correlation coefficient


Watermark embedding l.jpg
Watermark Embedding

Watermark

Original Image

Watermarked image

  • Watermark placed into information content of Original Image to create

  • Watermarked Image

  • Image Content

  • Spatial Domain (Least Significant Bit)

  • FFT - Magnitude and Phase

  • Wavelet Transforms

  • DCT Coefficients


Setup watermark embedding l.jpg
Setup-Watermark Embedding

DCT

IDCT

1000

Highest

Coeff

Water-

marked

Image

Image

Inter-

leave

Water-

mark

Conv

Code

  • DC Component Excluded for 1000 Highest Coefficients

  • Interleaving prevents burst errors

  • Watermarked Image Similar to original image

  • Without coding, ignore Conv Code and Interleave block


Slide8 l.jpg

Original Image

Watermarked Image, No Coding

  • 512x512 “Mandrill” Image

  • See Handout

  • Both watermarks imperceptible

  • Alterations to original image

  • difficult to notice

Watermarked Image with Coding


Watermark detection l.jpg
Watermark Detection

= 

*

Extracted

Watermark

Original

Watermark

Suspected Image

Correlation

  • Watermark Extracted from Suspected Image

  • Compute correlation of Extracted and Original Watermark

  • Threshold correlation to determine watermark existence


Watermark detection10 l.jpg
Watermark Detection

W2

Deinterleave,

Viterbi Decode

Correlation

Coefficient

Corrupted

Image

Extracted

Watermark

W1

Original

Image

1000 Highest

DCT Coeff

Owner’s

watermark

  • For no coding, deinterleave and decode block ignored

  • =E[W1*W2]/{ E[W12]E[W22]}

  • If W1=W2 then =1

  • if W1 and W2 are independent, then =0 if E[W1]=0

  • Corruptions are additive noise, JPEG Compression

  • Image scaling, and UnZign


Convolutional codes l.jpg
Convolutional Codes

C0

Input=[...1011010101100000000]

G0 = [1 1 1 1 0 1 0 1 1]

G1 = [1 0 1 1 1 0 0 0 1]

C1

  • Output C0 = conv(G0,Input); Output C1=conv(G1,Input)

  • Convolutional code implemented using linear shift registers

  • Adds redundancy for error-correction

  • Encoding/Decoding well researched

  • Good coding performance, very popular


Viterbi decoding l.jpg
Viterbi Decoding

State

0

1

2

3

  • Find most likely path through trellis

  • Begin and end at all zero state

  • Upper arrows => input=0, Lower arrow =>input=1

  • Every possible input/output combination is compared with the received output

  • Optimal Decoding Method


Slide13 l.jpg

No Coding:

Additive Noise(0,900)

With Coding:

Additive Noise (0,900)

  • Zero mean additive noise, variance=100, 400, 900

  • Both methods had high correlation

  • Coding method performed slightly better

  • For variance = 900

  •  (no coding) = 77%

  • p (coding) = 84%


Slide14 l.jpg

4:1 JPEG Compression,

No coding

4:1 JPEG Compression

With Coding

  • JPEG Compression: 1.4:1, 2.2:1, 4:1 ratio

  • Both methods resistant to JPEG compression

  • Coding method outperformed non-coding method

  • Perfect detection for coding method


Slide15 l.jpg

Watermark removal using Unzign

Convert to grayscale and resize

  • Unzign--watermark removal software

  • Image resized to 512x512 and convert to grayscale before detection

  • Moderate detection for without coding:

  • (no coding) = 57%

  • (coding) = 23%

  • Coding method sensitive to resizing


Conclusions l.jpg
Conclusions

  • Convolutional coding more immune to additive noise and

  • JPEG Compression

  • Coding method fragile w.r.t. rescaled images

  • Moderate detection levels for unzigned images

  • Further Suggestion:

  • Try block DCT

  • Use Wavelet Transform

  • Exploit Human Visual System



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