Data Hiding in Image and Video:Part II—Designs and Applications

Min Wu, Heather Yu, and Bede Liu

Outlines

Introduction

Multilevel Data Hiding in Grayscale Image

Multilevel Data Hiding in Video

Conclusion

Introduction

Goal: apply the solutions in Part I to specific design

problems and present details of embedding data

Multilevel Data Hiding in Grayscale Image

Introduction

Spectrum Partition

System Design

Experimental Results

Multilevel Data Hiding in Grayscale Image -- Introduction

Present a two-level data hiding using two types of embedding mechanisms Basis: Fig5. in Part I

Basic Assumptions/Conditions: Grayscale Images Embedding Domain: 8*8 block DCT coefficients Using Spectrum Segments for Embedding Dealing with non-coherent case

Multilevel Data Hiding in Grayscale Image -- Introduction

Spectrum Partition

Data Model and Formula

Experimental Results

Spectrum Partition-Data Model(1)

Embedding:

where the watermark {s1, …, sn } is an n-sample known

sequence, b: a bit to be embedded and is equally likely to be “-1”

or “+1”, di: noise, i.i.d. Gaussian

Spectrum Partition-Data Model(2)

A few considerations Bits can be embedded in all bands. In many

cases, bits are embedded in mid-band due to Low band coefficients generally have higher power High band coefficients are vulnerable to attacks

Noise Model can be extended to Normal Distribution with Various Covariance. Whitening should be performed in such cases

Spectrum Partition-Data Model(3)

The detector

The mean

Spectrum Partition-Simulation(1)

Subject: 141 ImagesEmbedding: the Block-DCT spread spectrum algorithm proposed by Podilchuk-ZengDetection: the q-statistic proposed by Zeng-LiuThree watermarks are usedPre-processing: An estimation of the host signal’s power is performed

based on testing images A set of known signals are added to help locating host

signal from noise

Spectrum Partition-Simulation(2)

Detection: Defined two statistics: q’ and q, with and without the weighting

Spectrum Partition-Simulation(3)

Experiments: DCT coefficients are ordered in zig-zag order Several distortion are introduced while computing q-statistics

JPEG with different quality factors Low pass filtering

q-statistics are normalized with respect to number of embeddable coefficients, see Figures

Q is maximum when the embedding starts around 6-11 Q’ is larger than q and it’s monotone

Conclusion: For high robustness, embed the bit to mid-band coefficients For high payload, embed the bit to low-band coefficients

Spectrum Partition-Simulation(4)

Spectrum Partition-Simulation(5)

Spectrum Partition-Simulation(6)

System Design

Block Diagram

Two Level Embedding

System Design– Block Diagram(1)

Embedding

System Design– Block Diagram(2)

Detecting

Two Level Embedding(1)

First Level: Using Odd-Even Embedding in the Low Band Quantization Techniques are applied

Two Level Embedding(2)

Second Level: Using Type I Spread Spectrum Technique Antipodal Modulation Is Used

where {vi}: original coefficients{vi’}: marked coefficients{b’}: antipodal mapping from b, which is +1 or –1 : watermark strength, adjusted by the just-noticeable-

difference (JND) standard

Experimental Results

Multilevel Data Hiding in Video

Embedding Domain

Variable Embedding Rate (VER) Versus Constant Embedding Rate (CER)

Control Data Versus User Data

Experimental Results

Embedding Domain(1)

Problems Introduced by Consecutive Frames Add/Drop Some Frames Switch the Order of Frames Generate New Frames

Possible Attacks Collusion Attack

Solution Adding Redundancy

Embedding Domain(2)

To Avoid Frame-Jitter Partitioning the Video into Temporal Segments Embedding Same Data in Every Frame of a

Segment

Embedding Domain(3)

To Avoid Frame Drop, Reordering, Insertion Embedding the Same User Data As Well As a

Shorten Version of Segment Index The Segment Index Is Part Of the Control Bits

Variable Embedding Rate (VER) vs. Constant Embedding Rate (CER)

Problem The Uneven Embedding Capacity Arises Both From

Region to Region within a Frame and From Frame to Frame

Solution Combine VER and CER

The Intra-Frame Unevenness Is Handled by CER and Shuffling

The Inter-Frame Unevenness Is Handled by VER and Additional Side Information

Number of Bits Embedded in Each Frame

Number of Bits That Can Be Embedded in Each Frame Changes Greatly Estimate Number of Bits for Each Frame Estimate the Achievable Embedding Payload

Based on Energy of DCT Coefficients, Number of Embeddable Coefficients

Set Two Threshold and If do not embed data If a number of bits are embedded

If bits are embedded in higher rate

1 2

C

1C 1 2C

2C

Estimation of Payload

For Type I Spread Spectrum Embedding, The Mean of Detection Statistic Is Bit Error Probability Is Given by Maximum Bit Error Probability Is Given by A Lower Bound of Mean Detection Statistic Is Defined by

The Detection Statistic When All Embeddable Coefficients Are Used Is Given By

The Payload Is

( )E T( ( ))Q E T

(max)eP

0T

1 (max)( )th eT Q P

Control Data Versus User Data(1)

Control Data: Additional Information Include Frame Sync Index, Number of Bits

Embedded in Each Frame

Embedding Frame Sync A Short Version of Video Segment Index Assume Frame Sync’s Range is 0 to K-1 The i-th Segment Is Labeled as mod( , )i K

Control Data Versus User Data(2)

User Data: Information

TDM with Shuffling IS Applied

Orthogonal Modulation Is Used to Double the Number of Embedded Bits Assume 2B bits Are Embedded

Block Diagram

Experimental Results

Conclusion

Demonstrate How to Apply General Solutions in Part I to Specific Designs

Made use of Two types of Embedding Modulation and Multiplex Techniques Shuffling Multilevel Data Hiding