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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

Digital Image Watermarking

Er-Hsien Fu

EE381K-15280

Student Presentation

overview
Overview
  • Introduction
  • Background

Watermark Properties

Embedding

Detection

  • The Project

Introduction

Embedding

Detection

  • Conclusions
introduction
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
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
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
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
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

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
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
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
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
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

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

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

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
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