HIGH CAPACITY WATERMARKING

HYPERSPECTRAL IMAGES AUTHENTICATION

Mehdi FallahpourJordi Serra-Ruiz

David Megías

 

Outline

1. Introduction

2. Image data hiding

3. Audio watermarking

4. Hyperspectral images authentication

5. Conclusions and future research

3

Sender

Receiver

Internet

illegalInformation

Watermarking

4

Sender

Receiver

Internet

illegal

Information

Information

Watermarking

5

Copyright protection was the original motivation

4 application categories

1. Copyright protection2. Hidden information3. Authentication4. Secure and invisible

communication

Watermarking applications

6

Imperceptibility Robustness

Capacity

Trade-off

Properties of digital watermarking

Outline

1. Introduction

2. Image data hiding

3. Audio watermarking

4. Hyperspectral images authentication

5. Conclusions and future research

High capacity, reversible data hiding inmedical images

Reversible Tiling and histogram shifting 30%-200% capacity improvement with still better

image quality compared with Ni et al. [63]

8

69. M. Fallahpour, D. Megías, M. Ghanbari, “High capacity, reversible data hiding in medical images”, IEEE International Conference on Image Processing, ICIP2009.

1st contribution Image Data Hiding

Reversible Data Hiding Based On H.264/AVC Intra Prediction

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68. M. Fallahpour; D. Megías. “Reversible Data Hiding Based On H.264/AVC Intra Prediction”, International Workshop on Digital Watermarking (IWDW 2008). Lecture Notes in Computer Science 5450, pp. 52–60, 2009.

Reversible Effective for removing error in H.264/Advanced

Video Coding Capacity is about 50 kbit and the PSNR of the

marked image is about 48 dB Improvement of capacity for the same transparency

1000% compared with Ni et al [63]

2nd contribution Image Data Hiding

Reversible image data hiding based on gradient adjusted prediction

Reversible Capacity is about 50 kbit and the PSNR of the

marked image is about 48 dB. Improvement of capacity under same transparency

1200%

10

92. M. Fallahpour, “Reversible image data hiding based on gradient adjusted prediction”, IEICE Electron. Express, Vol. 5, No. 20, pp.870-876, 2008. (Impactfactor 0.48 in 2008).

3rd contribution Image Data Hiding

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SchemeWhole

(1 block) Tiling

(4 blocks)Tiling

(16 blocks)

Intra prediction

based

GAP prediction

based

Reversibility Yes Yes Yes Yes Yes

Blind detection Yes Yes Yes Yes Yes

Capacity 2k to 10kIncreased by 10% to

100%

Increased by 50% to

400%

Increased by~ 1000%

Increased by~ 1200%

Transparency > 48 dB > 48 dB > 48 dB > 48 dB > 48 dB

Summary Image Data Hiding

Outline

1. Introduction

2. Image data hiding

3. Audio watermarking

4. Hyperspectral images authentication

5. Conclusions and future research

High capacity method for real-time audio data hiding using the FFT transform

Low complexity: a very efficient method for real-time applications

Based on FFT transform and using the histogram shifting idea

Very high capacity (5 kbps) Without significant perceptual distortion (ODG > – 1)

and robust against MP3-128

13

78. M. Fallahpour, D. Megías, “High capacity method for real-time audio data hiding using the FFT transform” Advances in Information Security and Its Application Third International Conference, ISA 2009, Springer, Seoul, Korea, June 25-27, 2009.

1st contribution Audio watermarking

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Robust high-capacity audio watermarking based on FFT amplitude modification

Very high capacity (about 5 kbps) No significant perceptual distortion Robustness against common audio signal

processing (added noise, filtering and MP3). Self-synchronization

80. M. Fallahpour, D. Megías, “Robust high-capacity audio watermarking based on FFT amplitude modification” IEICE Transactions on Information and Systems, Vol.E93-D,No.01, pp.-, Jan. 2010, in press (Impact factor 0.36 in 2008).

2nd contribution Audio watermarking

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Difference between the original and the MP3 compressed/decompressed

Improvements

Robustness against (13 attacks): MP3-128, AddBrumm, AddDynNoise, ADDFFTNoise, Addnoise, AddSinus, Amplify, FFT_Invert, FT_RealReverse, FFT_Stat1, Invert, RC_LowPass, RC_HighPass

Robustness against

13 attacks

Capacity1.5 kbps to

8.5 kbpsTransparency

Imperceptible, not annoying

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Experimental results

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High capacity audio watermarking using FFT amplitude interpolation

Take advantage of interpolation in FFT domainVery high capacity (about 3 kbps),No significant perceptual distortion (ODG about –0.5) Robustness against common audio signal processing

such as echo, add noise, filtering, resampling and MPEG compression (MP3).

79. M. Fallahpour, D. Megías, “High capacity audio watermarking using FFT amplitude interpolation”, IEICE Electron. Express, Vol. 6, No. 14, pp.1057-1063, 2009, (Impact factor 0.48 in 2008).

3rd contribution Audio watermarking

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Interpolation

Robustness against (18 attacks): AddBrumm, AddDynNoise, ADDFFTNoise, Addnoise, AddSinus,Amplify, BassBoost, Echo, FFT_HLPassQuick, FFT_Invert, Invert, Resampling, LSBZero, MP3, Noise_Max, Pitchscale, RC_HighPass, RC_LowPass

Robustness against

18 attacks

CapacityAbout 3 kbpsTransparency

Imperceptible, not annoying

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Experimental results

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DWT–based high capacity audio watermarking

High frequency band of the wavelet Divide the high frequency band into frames and then, for embedding, use the average of the relevant frame Very high capacity (about 5.5 kbps) Without significant perceptual distortion Robustness against common audio signal

81. M. Fallahpour, D. Megías, “DWT–based high capacity audio watermarking” IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, Vol.E93-A, No.01, pp.-, Jan. 2010, in press (Impact factor 0.43 in 2008).

4th contribution Audio watermarking

Robustness against (19 attacks):AddBrumm, Echo, AddDynNoise, AddFFTNoise, Addnoise, AddSinus, Amplify, BassBoost, FFT_HLPassQuick, FFT_Invert, FFT_RealReverse, Invert,LSBZero, MP3, Noise_Max, RC_HighPass, RC_LowPass, Smooth, Quantization

Robustness against

19 attacks

CapacityAbout 5.5 kbpsTransparency

Imperceptible, not annoying0 > ODG > –1

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Experimental results

[1* ]H. Kang, K. Yamaguchi, B. Kurkoski, K. Yamaguchi, and K. Kobayashi, “Full-Index-Embedding PatchworkAlgorithm for Audio Watermarking”, IEICE TRANS. On Information and Systems, E91-D(11):2731-2734, 2008[2*] J. J. Garcia-Hernandez, M. Nakano-Miyatake and H. Perez- Meana, “Data hiding in audio signal using Rational Dither Modulation”, IEICE Electron. Express, Vol. 5, No. 7, pp.217-222, 2008.[7*] M. A. Akhaee, M. J. Saberian, S. Feizi, F. Marvasti. “Robust Audio Data Hiding Using Correlated Quantization With Histogram-Based Detector” IEEE TRANS. ON Multimedia,V11, P 1-9, 2009.[4**] S. Xiang, H.J. Kim, J. Huang, “Audio watermarking robust against time-scale modification and MP3 compression,” Signal Processing, Vol.88 n.10, pp.2372-2387, October, 2008.

AlgorithmSNR (dB)

ODG of marked

Payload (bps) Robustness

Real time scheme [78] – –1 to 0 2.5 k to 8.5 k MP3

FFT scheme [80] 35 to 44 –1 to 0 1.5 k to 8.5 k 13

Interpolation scheme [79] 30.5 –1 to 0 3 k 18

Wavelet scheme [81] 33 –1 to 0 5.5 k 19

IEEE Trans. 2009 [7] 25 – 176 6

IEICE 2008 [1] 25 – 43 6

ELEX Trans. 2008 [2] – – 689 13

Signal Processing 2008 [4] 40 –2 to –1.5 2 18

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Comparison

Outline

1. Introduction

2. Image data hiding

3. Audio watermarking

4. Hyperspectral images authentication

5. Conclusions and future research

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Hyperspectral images

Signatures with real terrain information Images with multiple bands. Huge information High cost

Hyperspectral image authentication

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Tree vector quantization (TSVQ) and DWT-based hyperspectral images authentication

Some selected bands are marked. Wavelet Transform is applied. The watermark is a criterion over TSVQ. (32x32) pixels block tampering detection.

Hyperspectral image authentication

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70-80 dB of PSNR (original – marked image) means values modification: 15 – 20 (max. 12.000)

Experimental results

Difference histogramSignatures original and marked (shifted down 200 units)

Outline

1. Introduction

2. Image data hiding

3. Audio watermarking

4. Hyperspectral images authentication

5. Conclusions and future research

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Use histogram shifting (50 kbit & 48 dB)

Property Image DH

Capacity Transparency

Reversibility Blind detection

Conclusions and future research Image data hiding

Have a narrower histogram Ideal 256 kbit (1 bpp) under 48 dB Using histogram shifting in color images

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HAS helps to design Excellent capacity and transparency Robust against attacks

1. Configure the parameters that depend on the scheme

2. Repeating secret bits Frequency domain is complex and robust DWT or FFT

Conclusions (II)Conclusions and future research Audio watermarking

Real scenario (synchronization, real time)Improve robustnessTake advantage of wavelet transforms Repeat secret bits based on behavior of attacks

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