![]() In 2006 International Conference on Intelligent Information Hiding and Multimedia, (384–387).īanerjee, I., Bhattacharyya, S., & Sanyal, G. Analysis of current steganography tools: classifications & features. ![]() International Journal of Image, Graphics and Signal Processing, 7(6), 10.Ĭhen, M., Zhang, R., Niu, X., & Yang, Y. A comprehensive image steganography tool using LSB scheme. Applied Soft Computing, 34, 744–757.Įl Rahman, S. ![]() ![]() Fast retrieval of hidden data using enhanced hidden Markov model in video steganography. Institute of Technology Blanchardstown, Dublin, Ireland. In Proceedings of the Seventh IT&T Conference. A comparative analysis of steganographic tools. Multimedia Tools and Applications, 77(23), 31487–31516.Ĭheddad, A., Condell, J., Curran, K., & Mc Kevitt, P. High capacity, transparent and secure audio steganography model based on fractal coding and chaotic map in temporal domain. Improved image steganography based on super-pixel and coefficient-plane-selection. Steganalysis of OpenPuff through atomic concatenation of MP4 flags. This paper also analyze the performance of the OpenPuff tool on some unexplored parameters to validate and justify its performance. OpenPuff steganography tool spawns a huge acceptance by academics and professionals. The comparative analysis of these tools based on specified parameters represents their strengths, limitations, applicability, and scope for future work as well. In this paper, a systematic study of the steganography tools developed in the last three decades has been done. These spatial and transform domain tools implement different steganography techniques either solo or as a hybrid using a wide range of media as a cover file for hiding various types of data. The desire of industry and support from various governments motivated the researchers to develop steganography tools. Its specificity of concealing the existence of the secret data supports its application in securing the information in the modern era. This was also an interesting and informative read because it helped me determine better sensitivity settings for various stego algorithms to minimize False-Positives while at the same time reducing False-Negatives.Steganography emerged as an effective technology for securing the data over the network. It seems to show (compared to the study on False Positives) that a False-Negative is more likely than a False-Positive. One great thing about StegDetect is that it supports the ability to train the program on new, unknown algorithms as long as you have a clean set of images and a steg'ed set of images to 'train it' on.įor a savvy user, this sort of feature can help reduce the rate of False-Positives by improving the detection ability.Īnalysis of False-Negatives w/ StegDetect Nothing is perfect, and this is some research that was done on the reliability of StegDetect. StegDetect is a well known tool for detecting a variety of steganographic algorithms. ![]() Steganography For The Computer Forensics ExaminerĪ good primer on steganography in relation to 'the real world'Īnalysis of False-Positives w/ StegDetect Many different methods and programs exist to try and suss out whether an image is steg'ed or not, but here's a couple links to research papers on the reliability of certain tools: there's no guarantee it's THE ONLY needle in there! And worst of all, even if you find a needle. There are new methods of steganography being developed and theorized all the time, so detecting it is quite literally like looking for a needle in a haystack. ![]()
0 Comments
Leave a Reply. |