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H IGH CAPACITY WATERMARKING H YPERSPECTRAL I MAGES AUTHENTICATION Mehdi Fallahpour Jordi Serra-Ruiz David Megías.

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Presentation on theme: "H IGH CAPACITY WATERMARKING H YPERSPECTRAL I MAGES AUTHENTICATION Mehdi Fallahpour Jordi Serra-Ruiz David Megías."— Presentation transcript:

1 H IGH CAPACITY WATERMARKING H YPERSPECTRAL I MAGES AUTHENTICATION Mehdi Fallahpour Jordi Serra-Ruiz David Megías

2 Outline 1. Introduction 2. Image data hiding 3. Audio watermarking 4. Hyperspectral images authentication 5. Conclusions and future research

3 3 Sender Receiver Internet illegal Information Watermarking

4 4 Sender Receiver Internet illegal Information Information Watermarking

5 5  Copyright protection was the original motivation  4 application categories 1. Copyright protection 2. Hidden information 3. Authentication 4. Secure and invisible communication Watermarking applications

6 6 Imperceptibility Robustness Capacity Trade-off Properties of digital watermarking

7 Outline 1. Introduction 2. Image data hiding 3. Audio watermarking 4. Hyperspectral images authentication 5. Conclusions and future research

8 High capacity, reversible data hiding in medical images  Reversible  Tiling and histogram shifting  30%-200% capacity improvement with still better image quality compared with Ni et al. [63] M. Fallahpour, D. Megías, M. Ghanbari, “High capacity, reversible data hiding in medical images”, IEEE International Conference on Image Processing, ICIP st contribution Image Data Hiding

9 Reversible Data Hiding Based On H.264/AVC Intra Prediction 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,  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] 2 nd contribution Image Data Hiding

10 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% M. Fallahpour, “Reversible image data hiding based on gradient adjusted prediction”, IEICE Electron. Express, Vol. 5, No. 20, pp , (Impact factor 0.48 in 2008). 3 rd contribution Image Data Hiding

11 11 Scheme Whole (1 block) Tiling (4 blocks) Tiling (16 blocks) Intra prediction based GAP prediction based ReversibilityYes Blind detectionYes Capacity2k to 10k Increased by 10% to 100% Increased by 50% to 400% Increased by ~ 1000% Increased by ~ 1200% Transparency> 48 dB Summary Image Data Hiding

12 Outline 1. Introduction 2. Image data hiding 3. Audio watermarking 4. Hyperspectral images authentication 5. Conclusions and future research

13 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 MP 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, st contribution Audio watermarking

14 14 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). 2 nd contribution Audio watermarking

15 15 Difference between the original and the MP3 compressed/decompressed Improvements

16 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 Capacity 1.5 kbps to 8.5 kbps Transparency Imperceptible, not annoying 16 Experimental results

17 17 High capacity audio watermarking using FFT amplitude interpolation Take advantage of interpolation in FFT domain Very 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 , 2009, (Impact factor 0.48 in 2008). 3 rd contribution Audio watermarking

18 18 Interpolation

19 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 Capacity About 3 kbps Transparency Imperceptible, not annoying 19 Experimental results

20 20 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). 4 th contribution Audio watermarking

21 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 Capacity About 5.5 kbps Transparency Imperceptible, not annoying 0 > ODG > –1 21 Experimental results

22 [1 * ]H. Kang, K. Yamaguchi, B. Kurkoski, K. Yamaguchi, and K. Kobayashi, “Full-Index-Embedding Patchwork Algorithm for Audio Watermarking”, IEICE TRANS. On Information and Systems, E91-D(11): , 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 , [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, [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 , October, Algorithm SNR (dB) ODG of marked Payload (bps) Robustness Real time scheme [78]––1 to 02.5 k to 8.5 kMP3 FFT scheme [80]35 to 44–1 to 01.5 k to 8.5 k13 Interpolation scheme [79]30.5–1 to 03 k18 Wavelet scheme [81]33–1 to 05.5 k19 IEEE Trans [7]25–1766 IEICE 2008 [1]25–436 ELEX Trans [2]––68913 Signal Processing 2008 [4] 40–2 to – Comparison

23 Outline 1. Introduction 2. Image data hiding 3. Audio watermarking 4. Hyperspectral images authentication 5. Conclusions and future research

24 24 Hyperspectral images Signatures with real terrain information Images with multiple bands. Huge information High cost Hyperspectral image authentication

25 25 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

26 dB of PSNR (original – marked image) means values modification: 15 – 20 (max ) Experimental results Difference histogram Signatures original and marked (shifted down 200 units)

27 Outline 1. Introduction 2. Image data hiding 3. Audio watermarking 4. Hyperspectral images authentication 5. Conclusions and future research

28 28 Use histogram shifting (50 kbit & 48 dB) PropertyImage 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

29 29 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 robustness Take advantage of wavelet transforms Repeat secret bits based on behavior of attacks

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