1. Digital Image Watermarking using Hybrid DWT -FFT Technique with Different Attacks Presented By: Mahendra Kumar Faculty at UCE, RTU, Kota (Raj.) India Director, MI Tech Society, Kota (Raj.) India Authors: Reema Jain, Mahendra Kumar, Arihant Kumar Jain, and Manish Jain

2. Contents 1.Motivation and Problem statement 2.Classification of watermark Algorithms 3.DWT & FFT Transform 4.Proposed Hybrid Technique 5.Experimental results 6.Conclusion 7.References

3. In digital watermarking, a watermark is embedded into a cover image in such a way that the resulting watermarked signal is robust to certain distortions caused by either standard data processing in a friendly environment or malicious attacks in an unfriendly environment. In previous techniques when apply attacks, the extract watermark is poor quality, so this paper proposed a novel hybrid transform technique which provide extract watermark with better quality. This work provides Digital Image Watermarking based on Hybrid DWT-FFT with different malicious attacks (JPEG compression, Salt & Peppers Noise, Gaussian Noise, Blurring and Blurring & Noise). Signal to Noise Ratio (SNR) is computed to measure image quality for proposed technique for better results as compared to previous techniques of information hiding. Introduction

4. Classification of Watermark Algorithms  Visible Watermarking (alpha>0.1): Visible watermarking is easily perception by the human eye, means the visible watermark can be seen without the extraction process. For example it can be name or logo of the company.  Invisible Watermarking (alpha<0.1): In this watermarking mark cannot be seen by human eye. It is embedded in the data without affecting the content and can be extracted by the owner only.

5. Basic Requirement for a Digital Watermark Algorithm Figure 1: Watermark Embedding Process Figure 2: Watermark Extraction Process

6. Distortions and Attacks  Hostile or malicious attacks, which are an attempt to weaken, remove or alter the watermark, and  Coincidental attacks, which can occur during common image processing and are not aimed at tampering with the watermark. Removal attacks attempt to separate and remove the watermark. Compression: Practically all images currently being distributed via Internet have been compressed.

7. Discrete Wavelet Transform Different projection spaces different signal representations Signal Domain Projection space, Basis functions Transformed Domain Transformation

8. Sub-bands  LL band = Coarser approximation to the original image.  LH and HL = Record the changes of the image along horizontal and vertical directions, respectively.  HH band = Shows the high frequency component of the image 8 LLHL LHHH

9. Advantage of Discrete Wavelet Transform....  DWT provides a compact representation of a signal frequency components with strong spatial support.  DWT decomposes a signal into frequency sub-bands at different scales from which it can be perfectly reconstructed.  DWT reduces the computational complexity(separate into H & L freq.)  It gives information about both time and frequency of the signal  Transform of a non-stationary signal is efficiently obtained,  reduces the size without losing much of resolution, Reduces redundancy and Reduces computational time. 9

10. Two Dimensional Analysis  DWT using the simple Haar wavelet transform, the two dimensional Haar transform can be defined easily in terms of the one dimensional Haar.  2D-DWT consists of two phases 1.The first phase is applying the one dimensional DWT on the two dimensional input signal in row/column based order. 2.The second phase is applying the one dimensional DWT on the output of the first phase in column/row based order. 10

11. Illustration of 2-D wavelet transform 11

12. The FFT is applied on spatial domain image to obtain FFT coefficients. The features that are extracted from FFT coefficients are real part, imaginary part, magnitude value and phase angle. The features of DWT are obtained from approximation band only. The features of FFT are computed using the magnitude values. Fast Fourier Transform (FFT)

13. PROPOSED TECHNIQUE

14. Quality Measurements In order to evaluate the quality of watermarked image, the following signal- to-noise ratio (SNR)

15. Experimental Results Of Proposed Technique Original image Embedding watermark image

16. TABLE 1 Proposed Hybrid Technique ATTACKS Watermarked Image (SNR) Extracted Watermark (SNR) JPEG Compression Q=90% 48.581857.3402 Salt & Peppers Noise43.136541.4808 Blurring47.393535.4787 Blur + Gaussian Noise36.236725.0728

17. Conclusion This paper provides Digital Image Watermarking based on Hybrid 3-Level DWT - FFT algorithm with different malicious attacks (JPEG compression, Salt & Peppers Noise, Gaussian noise, Blurring and Blurring & Noise), Table 1 shows the effectiveness of the proposed technique in terms of PSNR of watermarked image & extracted watermark. In future, apply DCT based pyramid laplacian transform for watermarking.

18. REFERENCES [1]Edin Muharemagic and Borko Furht, “Survey Of Watermarking Techniques And Applications”, Department of Computer Science and Engineering, Florida Atlantic University. [2]Andreja Samˇcovi´c, J´an Tur´an, “Attacks on Digital Wavelet Image watermarks”, Journal of Electrical Engineering. [3]Peining Taoa and Ahmet M. Eskicioglub, “A robust multiple watermarking scheme in the Discrete Wavelet Transform domain”, The Graduate Center, The City University of New York. [4]Baisa L. Gunjal, “An Overview Of Transform Domain Robust Digital Image Watermarking Algorithms”, Department of Computer Engineering, Amrutvahini College of Engineering. [5]Igor Djurovic, Srdjan Stankovic, and Ioannis Pitas,” Digital watermarking in the fractional Fourier transformation domain”, Journal of Network and Computer Applications (2001), page 167 – 173. [6]Vaishali.S.Jabade, Dr.Sachin R.Gengaje “Literature Review of Wavelet based Digital Image Watermarking Techniques”, International Journal of Computer Applications, Vol.31, No.1, October2011. [7]P. Meerwald, A. Uhl, “A Survey of Wavelet-DomainWatermarking Algorithms”, EI San Jose, CA, USA, 2001. [8]Mohamed A. Suhail and Mohammad S. Obaidat, "Digital Watermarking-Based DCT and JPEG Model", IEEE Transactions On Instrumentation and Measurement, Vol. 52, NO. 5, p.1640-1647, October 2003. [9] Mahendra Kumar et. al., “Implementation of Different Non-Recursive FIR Band-pass filters using Fractional Fourier Transform” in proceedings of 4 th IEEE International Conference on Computational Intelligence and Communication Networks (CICN-2012), Mathura, 3-5 Nov. 2012. [10] Mahendra Kumar et. al., “Digital image watermarking: A survey”, International Journal of Engineering and research applications (IJERA), Jul-Aug, 2013. Jul-Aug, 2013. [11] Mahendra Kumar et.al., “Digital Image Watermarking using Fractional Fourier transform via image compression”, In IEEE International Conference on Computational Intelligence and Computing Research 2013 (IEEE ICCIC-2013), 26-28 Dec., 2013. [12] Mahendra Kumar et. al., “Digital Image Watermarking using Fractional Fourier Transform with Different Attacks” International Journal of Scientific Engineering and Technology, Volume No. 3 Issue No. 8, Aug. 2014, pp: 1008-1011, (ISSN : 2277-1581).