Image Authentication Based on Neural Networks

Shiguo Lian

(Submitted on 31 Jul 2007)

Abstract: Neural network has been attracting more and more researchers since the past decades. The properties, such as parameter sensitivity, random similarity, learning ability, etc., make it suitable for information protection, such as data encryption, data authentication, intrusion detection, etc. In this paper, by investigating neural networks’ properties, the low-cost authentication method based on neural networks is proposed and used to authenticate images or videos. The authentication method can detect whether the images or videos are modified maliciously. Firstly, this chapter introduces neural networks’ properties, such as parameter sensitivity, random similarity, diffusion property, confusion property, one-way property, etc. Secondly, the chapter gives an introduction to neural network based protection methods. Thirdly, an image or video authentication scheme based on neural networks is presented, and its performances, including security, robustness and efficiency, are analyzed. Finally, conclusions are drawn, and some open issues in this field are presented.

Here the file ini pdf

PASSaGE: Software for Searching Biological Image

Researchers at the Arizona State University (ASU) are working on software tools to analyze databases of biological images, that’s called PASSaGE (Pattern Analysis, Spatial Statistics and Geographic Exegesis). One of these projects is using machine learning technology to compare the expression patterns captured in the images. So far, the software was used to explore a database of embryonic fruit flies images to see if the genes share the same spatial patterns. This would indicate that these genes also share similar functions. The goal of the developers is to build a tool able to search biological image databases as fast as Internet search engines are doing.

If you want more information about this project, you can read a paper presented last year at the Computational Systems Bioinformatics Conference (CSB), “Classification of Drosophila embryonic developmental stage range based on gene expression pattern images” (PDF format, 6 pages, 2.52 MB).

Overview of PASSaGE

Spatial analysis is a fundamental part of scientific inquiry, including ecological, evolutionary and environmental science, epidemiology, geology, geography, and mathematics. Recent technological advances in genome sequencing, global positioning systems, and remote sensing have led to a rapid expansion of the number and size of spatially explicit datasets available for analysis. These new data have advanced the scope of spatial analysis to an even braoder variety of human endeavors, but have also rapidly outpaced the capabilities of traditional spatial analytic software and methods.

The need to overcome data limitations inherent in much of the specialized spatial analysis programs commonly available led to the development of PASSaGE: Pattern Analysis, Spatial Statistics, and Geographic Exegesis, a free, easy-to-use program for general spatial analysis. With a fairly simple point-and-click, mouse- and menu-driven interface, but flexible and powerful analysis customization, PASSaGE has been a very popular system for analyzing data in spatial context in both the laboratory and the classroom. The first version of PASSaGE has been downloaded by thousands of users from over 57 countries and 145 U.S. universities.

The Software PASSaGe is free (Windows, Linux, and Macintosh) and could be download here

As an example, you can see on the left a collage of fruit fly gene expression images. “The proper development of each football-shaped fly embryo depends on the coordinated expression of thousands of genes. By studying the expression pattern of single genes, typically displayed in wide bands or narrow striped patterns, scientists can gain insight into the control and regulation of large genetic networks. Similar gene networks are found throughout biology, and break downs in these processes may result in birth defects, heart disease, cancer and aging. (Credit for image and caption: Biodesign News at ASU)

Program SARMAG dan Pasca-Sarjana: Seminar dan Kuliah

Program Sarmag dan Pasca-Sarjana Universitas Gunadarma telah mengadakan seminar dan kuliah (2 April- 10 April 2007) terkait dengan Infrared Imaging dan Computer Vision. Seminar dan kuliah diberikan oleh Prof. MERIAUDEAU FABRICE dari Universite’ de Bourgogne (France) dan Prof. Sarifuddin Madenda (Quebec, Canada and Gunadarma University)). Bahan kuliah dan seminar dapat di download di sini (site1, site2)

Disamping kuliah dan Seminar, telah berlangsung juga penandatangan MOU antara Universitas Gundaram (Program Sarmag) dan Universite’ de Bourgogne (Le2I) .

Quantum Discrete Cosine Transform for Image Compression

Chao Yang Pang, et.al

Abstract: Discrete Cosine Transform (DCT) is very important in image compression. Classical 1-D DCT and 2-D DCT has time complexity O(NlogN) and O(N²logN) respectively. This paper presents a quantum DCT iteration, and constructs a quantum 1-D and 2-D DCT algorithm for image compression by using the iteration. The presented 1-D and 2-D DCT has time complexity O(sqrt(N)) and O(N) respectively. In addition, the method presented in this paper generalizes the famous Grover’s algorithm to solve complex unstructured search problem.

file here ps, pdf

Half-Day Seminar: Advanced Research on Image Processing

Universitas Gunadarma menyelenggarakan seminar 1/2 hari dengan tema “Advanced Research on Image Processing”, kamis 25 Januari 2007.

Pembicara:

1. Dewi Agushinta MSc, Program S3 TI Universitas Gunadarma, Ekstrasi dan Segmentasi Wajah sebagai Semantik pada Pengenalan Wajah (pdf file)
2. Prof. M. Paindavoine, L2I Univeristat de Bourgogne, Implementation of Image Processing onto FPGA using Modular DSP C6201 VHDL Model (ppt files)

Seminar menggunakan fasilitas “Teleconference” Uni-Gunadarma (Hasil Hibah Project Inherent K3 DIKTI) bagi para audience/peserta di ITS, Surabaya.