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”, d i : 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 Images Embedding: the Block-DCT spread spectrum algorithm proposed by Podilchuk-Zeng Detection: the q-statistic proposed by Zeng-Liu Three watermarks are used Pre-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 {v i }: original coefficients {v i ’}: 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

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

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

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