We might have collaborated with health professionals in the community, schools, or other organizations to help address the community response to COVID-19. COVID-19 might have posed research questions and opened a new line of research. For some, COVID-19 may have opened new doors. As a result, it is a good idea to monitor and document the various ways our academic work has changed as a result of COVID-19. When the time comes to submit an annual evaluation at the close of the Spring Term or to prepare a tenure and promotion portfolio for future years, we might struggle to remember how we spent our time and what we accomplished during the time of COVID-19. During the past year, we have encountered numerous challenges (and sometimes opportunities) driven by social distancing mandates and the need to keep family, neighbors, students, and colleagues safe. During the pivot to remote instruction, faculty made a variety of adjustments to teaching and research agendas. During COVID-19, we might struggle to remember what we ate for breakfast, much less remember all the challenges we faced last week. Memory is fleeting under the best of circumstances. Shunquan Tan, Bin Li, Targeted Steganalysis of Edge Adaptive Image Steganography Based on LSB Matching Revisited Using B-Spline Fitting, IEEE Signal processing, vol.19, n.6, pp.336-339, June 2012.Previous Next Document the impact of COVID-19 on your academic workĭocument the impact of COVID-19 on your academic work Natarajan Meghanathan, Lopamudra Nayak, Steganalysis Algorithms For Detecting The Hidden Information In Image, Audio And Video Cover Media, International Journal of Network Security & Its Application (IJNSA), Vol.2, No.1, pp.43-55, January 2010.ĭiyanat, Farhat, Ghaemmaghami, Image steganalysis based on SVD and noise estimation: Improve sensitivity to spatial LSB embedding families, TENCON 2011 - 2011 IEEE Region 10 Conference, Nov.2011. Manjunatha Reddy, K.B.Raja Venugopal, L.M.Patnaik, Detecting Original image Using Histogram, DFT and SVM, ACEEE International Journal on Signal & Image Processing, Vol.1, n.1, pp. Heileman, Model-based steganalysis using invariant features, Spieei09, 2009. Tu-Thach Quacha, Fernando P´erez-Gonz´alezb, Gregory L. Swaminathan, Min Wu, Noise Features On Image Tampering Detection and Steganalysis, IEEE International Conference on Image processing 2007. Faez, Data Hiding Detection Based on DWT and Zernike Moments, SETIT 2007 International Conference: Sciences of Electronic, Technologies of Information and Telecommunications March 25-29, 2007.Ī. Lu Zhiwu, Lu Xiaoqing, Wavelet Statistics for Steganalysis Using Image Noise, High Technology Letters, vol. Tomáš Pevný, Patrick Bas, Jessica Fridrich, Steganalysis by Subtractive Pixel Adjacency Matrix, IEEE transactions on Information and security, vol.5 n.5, pp. Wen-Nung Lie, Guo-Shiang Lin, A Feature-Based Classification Technique For Blind Image Steganalysis, IEEE Transcations On Multimedia, vol.7, n.6, pp.Dec 2005. Shi, Jianjiong Gao, Dekun Zou, Chengyun Yang, Zhenping Zhang, Peiqi Chai, Chunhua Chen, Wen Chen, Steganalysis Based on Multiple Features Formed by Statistical Moments of Wavelet Characteristic Functions, Lecture notes in computer science: 7th International Workshop on Information Hiding in 2005. Sos Agaian, Hong Cai, Color Wavelet Based Universal Blind Steganalysis, International TICSP workshop on Spectral methods and Multirate Signal processing(SMMSP 2004), Vienna, Austria, (pp 183-189Sep 11-12, 2004). Siwei Lyu, Hany Farid, Steganalysis Using Higher-Order Image Statistics, IEEE transactions on Information Forensics and Security, vol. The experimental results demonstrated that the proposed method can effectively identify digital images from their tampered or stego versions and can also successfully classify the steganographic algorithm with which the secret was embedded into the cover image.Ĭopyright © 2013 Praise Worthy Prize - All rights reserved. The performance of the Steganalyzer is also analyzed with different number of training, testing subjects. In order to improve their performance, ensemble classifier is used. A new set of features including Noise features, Zernike moments, Moments of Characteristic Function (MOCF), Colour, Fourier Descriptors are extracted independently and are given to different classifiers including Minimum Distance Classifier (MDC), Least Squares Support Vector Machine (LS SVM), OSU SVM etc and their steganalytic performance is analysed. Identifying the type of embedding algorithm might lead to extraction of the hidden image. In this paper, generic as well as analytic steganalysis method is used to detect the presence of hidden image and also the tool used to embed the secret image in the cover image.