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

Plagiarism Detection Tools

Automatic Generation of Benchmarks for Plagiarism Detection Tools using Grammatical Evolution

Authors: Manuel Cebrian, Manuel Alfonseca, Alfonso Ortega
Comments: 8 pages, 9 figures. Extended version of the poster accepted in GECCO’07

Student plagiarism is a mayor problem in universities worldwide. In this paper, we focus on plagiarism in answers to computer programming assignments, where student mix and/or modify one or more original solutions to obtain counterfeits. Although several software tools have been implemented to help the tedious and time consuming task of detecting plagiarism, little has been done to assess their quality, because, in fact, determining the original subset of the whole solution set is practically impossible for graders. In this article we present a Grammatical Evolution technique (http://www.grammatical-evolution.org/ or http://www.grammaticalevolution.org/ )which generates benchmarks. Given a programming language, our technique generates a set of original solutions to an assignment, together with a set of plagiarisms of the former set which mimic the way in which students act. The phylogeny of the coded solutions is predefined, providing a base for the evaluation of the performance of copy-catching tools. We give empirical evidence of the suitability of our approach by studying the behavior of one state-of-the-art detection tool (AC) on four benchmarks coded in APL2, generated with this technique.

http://arxiv.org/abs/cs.NE/0703134