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Chincholkar, Y. D.
- Effective Robust Patchwork Method to the Vulnerable Attack for Digital Audio Watermarking
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Authors
Affiliations
1 Department of Electronics and Telecommunications Engineering, Savitribai Phule Pune University, IN
1 Department of Electronics and Telecommunications Engineering, Savitribai Phule Pune University, IN
Source
ICTACT Journal on Image and Video Processing, Vol 8, No 4 (2018), Pagination: 1753-1758Abstract
This paper presents patchwork based digital audio watermarking. The advanced growth in transmission of digital data has resulted in a corresponding elevation in the need for copyright protection of signal. Cryptography and steganography are used for the content protection but do not completely solve the copyright issue. Watermarking is a method to protect and identify the digital data while maintaining the quality of the host media, it permits various types of watermarks to be hidden in audio signal e.g. image, audio and video. This paper limits on image embedding technique using patchwork-based method. In patchwork based method average of all segments of approximate coefficients is calculated for embedding watermark into sound signal. The experimental results shows that proposed method achieves imperceptibility for audio signal as watermarked audio signal is inaudible after embedding watermark and robustness of watermark against different signal processing attacks with higher PSNR. The resulting audio is robust to attacks and exhibits good quality in term of peak signal to noise ratio. The simulation results show the effectiveness of the proposed system.Keywords
Audio Watermarking, Stationary Wavelet Transform, Patchwork.References
- Y. Xiang, I. Natgunanathan, S. Guo, W. Zhou and S. Nahavandi, “Patchwork-Based Audio Watermarking Method Robust to Desynchronization Attacks”, IEEE/ACM Transactions on Audio, Speech, and Language Processing, Vol. 22, No. 9, pp. 1413-1423, 2014.
- Pranab Kumar Dhar and Tetsuya Shimamura. “Blind SVDbased Audio Watermarking using Entropy and Log-Polar Transformation”, Journal of Information Security and Applications, Vol. 20, pp. 74-83, 2015.
- H. Huang, C. Yang and W. Hsu, “A Video Watermarking Technique based on Pseudo-3-D DCT and Quantization Index Modulation”, IEEE Transactions on Information Forensics and Security, Vol. 5, No. 4, pp. 625-637, 2010.
- F. Guerrini, M. Okuda, N. Adami and R. Leonardi, “High Dynamic Range Image Watermarking Robust against ToneMapping Operators”, IEEE Transactions on Information Forensics and Security, Vol. 6, No. 2, pp. 283-295, 2011.
- K. Yeo and H.J. Kim, “Modified Patchwork Algorithm: A Novel Audio Watermarking Scheme”, IEEE Transactions on Speech and Audio Processing, Vol. 11, No. 4, pp. 381-386, 2003.
- W.N. Lie and L.C. Chang, “Robust and High-Quality TimeDomain Audio Watermarking based on Low-Frequency Amplitude Modification”, IEEE Transactions on Multimedia, Vol. 8, No. 1, pp. 46-59, 2006.
- Y.D. Chincholkar, S.R. Ganorkar and A.S. Sawai , “Implementation Of Audio Watermarking Technique For Copyright Protection using SWT Algorithm”, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 4, No. 3, pp. 42-48, 2016.
- X. Wang, W. Qi and P. Niu, “A New Adaptive Digital Audio Watermarking based on Support Vector Regression”, IEEE/ACM Transactions on Audio Speech and Language Processing, Vol. 15, No. 8, pp. 2270-2277, 2007.
- S. Valizadeh and Z.J. Wang, “Correlation-and-Bit-Aware Spread Spectrum Embedding for Data Hiding”, IEEE Transactions on Information Forensics Security, Vol. 6, No. 2, pp. 267-282, 2011.
- S. Valizadeh and Z. J.Wang, “An Improved Multiplicative Spread Spectrum Embedding Scheme for Data Hiding”, IEEE Transactions on Information Forensics Security, Vol. 7, No. 4, pp. 1127-1143, 2012.
- X. Wang, W. Qi, and P. Niu, “A New Adaptive Digital Audio Watermarking based on Support Vector Regression”, IEEE/ACM Transactions on Audio Speech and Language Processing, Vol. 15, No. 8, pp. 2270-2277, 2007.
- D. Lakshmi, R. Ganesh, R. Marni, R. Prakash and P. Arulmozhivarman, “SVM based Effective Watermarking Scheme for Embedding Binary Logo and Audio Signals in Images”, Proceedings of IEEE Region 10 Conference, pp. 1-5, 2008.
- M. Arnold, “Audio Watermarking: Features, Applications and Algorithm”, Proceedings of IEEE International Conference on Multimedia Expo, pp. 1013-1016, 2000.
- Traffic Sign Board Detection and Recognition for Autonomous Vehicles and Driver Assistance Systems
Abstract Views :253 |
PDF Views:0
Authors
Affiliations
1 Department of Electronics and Telecommunication Engineering, Sinhgad College of Engineering, IN
1 Department of Electronics and Telecommunication Engineering, Sinhgad College of Engineering, IN
Source
ICTACT Journal on Image and Video Processing, Vol 9, No 3 (2019), Pagination: 1954-1959Abstract
In the recent year's many approaches have been made that uses image processing algorithms to detect traffic sign boards. Edge detection is used to avoid segmentation problems of the existing method. Color based segmentation faces the challenge of adaptive thresholding which fails in real time scenarios. This proposed algorithm is yet another approach to detect traffic sign boards from video sequences. The first step of this work is the pre-processing of the video frame which is achieved by the gray scale conversion and edge detection and the second step is the extraction of the objects. Hough Transform algorithm is then applied to measure properties of image regions for further analysis. The different feature points which include perimeter, area, filled area, solidity and centroid are extracted for the detection of the traffic sign board. Feature generation and classification are done on the recognition side to get the class of the detected object. The input for the project is video sequences taken from a camera placed on the vehicle.Keywords
Hough Transform, Machine Learning Algorithm, Traffic Detection, Feature Classification.References
- Zhihui Zheng, Bo Wang and Zhifeng Gao, “Robust Traffic Sign Recognition and Tracking for Advanced Driver Assistance Systems”, Proceedings of 15th International IEEE Conference on Intelligent Transportation System, pp. 704-709, 2012.
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- N. Barnes and A. Zelinsky, “Real-Time Radial Symmetry for Speed Sign Detection”, Proceedings of International Symposium on Intelligent Vehicles, pp. 566-571, 2004.
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- GiYeong Bae, JeongMok Ha, Jea Young Jeon, Sung Yong Jo and Hong Jeong, “LED Traffic Sign Detection using Rectangular Hough Transform”, Proceedings of International Conference on Information Science and Applications, pp. 1-4, 2014.
- Seokwoo Jung, Unghui Lee, Jiwan Jung and David Hyunchul Shim, “Real Time Traffic Sign Board Recognition with Deep Convolutional Neural Network”, Proceedings of International Conference on Ubiquitous Robots and Ambient Intelligence, pp. 23-28, 2016.
- Zhihui Zheng, Hanxizi Zhang, Bo Wang and Zhifeng Gao, “Robust Traffic Sign Recognition and Tracking for Advanced Driver Assistance Systems”, Proceedings of 15th International Conference on Intelligent Transportation Systems, pp. 704-709, 2012.
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