This paper presents a universal scheme （also called blind scheme） based on fractal compression and affinity propagation （AP） clustering to distinguish stego-images from cover grayscale images, which is a very chal...This paper presents a universal scheme （also called blind scheme） based on fractal compression and affinity propagation （AP） clustering to distinguish stego-images from cover grayscale images, which is a very challenging problem in steganalysis. Since fractal codes represent the ＂self-similarity＂ features of natural images, we adopt the statistical moment of fractal codes as the image features. We first build an image set to store the statistical features without hidden messages, of natural images with and and then apply the AP clustering technique to group this set. The experimental result shows that the proposed scheme performs better than Fridrich＇s traditional method.展开更多
The work on the paper is focused on the use of Fractal Dimension in clustering for evolving data streams. Recently Anuradha et al. proposed a new approach based on Relative Change in Fractal Dimension (RCFD) and dampe...The work on the paper is focused on the use of Fractal Dimension in clustering for evolving data streams. Recently Anuradha et al. proposed a new approach based on Relative Change in Fractal Dimension (RCFD) and damped window model for clustering evolving data streams. Through observations on the aforementioned referred paper, this paper reveals that the formation of quality cluster is heavily predominant on the suitable selection of threshold value. In the above-mentionedpaper Anuradha et al. have used a heuristic approach for fixing the threshold value. Although the outcome of the approach is acceptable, however, the approach is purely based on random selection and has no basis to claim the acceptability in general. In this paper a novel method is proposed to optimally compute threshold value using a population based randomized approach known as particle swarm optimization (PSO). Simulations are done on two huge data sets KDD Cup 1999 data set and the Forest Covertype data set and the results of the cluster quality are compared with the fixed approach. The comparison reveals that the chosen value of threshold by Anuradha et al., is robust and can be used with confidence.展开更多
A modified fractal growth model based on the deposition, diffusion, and aggregation (DDA) with cluster rotation is presented to simulate two-dimensional fractal aggregation on liquid surfaces. The mobility (including ...A modified fractal growth model based on the deposition, diffusion, and aggregation (DDA) with cluster rotation is presented to simulate two-dimensional fractal aggregation on liquid surfaces. The mobility (including diffusion and rotation) of clusters is related to its mass, which is given by Dm = D0s-γD and θm = θ0s-γθ, respectively. We concentrate on revealing the details of the influence of deposition flux F, cluster diffusion factor γD and cluster rotation factor γθ on the dynamics of fractal aggregation on liquid surfaces. It is shown that the morphologies of clusters and values of cluster density and fractal dimension depend dramatically on the deposition flux and migration factors of clusters.展开更多
基金supported by the National Natural Science Foundation of China under Grant No. 61070208the Postdoctor Foundation from North Electronic Systems Engineering Corporation
文摘This paper presents a universal scheme （also called blind scheme） based on fractal compression and affinity propagation （AP） clustering to distinguish stego-images from cover grayscale images, which is a very challenging problem in steganalysis. Since fractal codes represent the ＂self-similarity＂ features of natural images, we adopt the statistical moment of fractal codes as the image features. We first build an image set to store the statistical features without hidden messages, of natural images with and and then apply the AP clustering technique to group this set. The experimental result shows that the proposed scheme performs better than Fridrich＇s traditional method.
文摘The work on the paper is focused on the use of Fractal Dimension in clustering for evolving data streams. Recently Anuradha et al. proposed a new approach based on Relative Change in Fractal Dimension (RCFD) and damped window model for clustering evolving data streams. Through observations on the aforementioned referred paper, this paper reveals that the formation of quality cluster is heavily predominant on the suitable selection of threshold value. In the above-mentionedpaper Anuradha et al. have used a heuristic approach for fixing the threshold value. Although the outcome of the approach is acceptable, however, the approach is purely based on random selection and has no basis to claim the acceptability in general. In this paper a novel method is proposed to optimally compute threshold value using a population based randomized approach known as particle swarm optimization (PSO). Simulations are done on two huge data sets KDD Cup 1999 data set and the Forest Covertype data set and the results of the cluster quality are compared with the fixed approach. The comparison reveals that the chosen value of threshold by Anuradha et al., is robust and can be used with confidence.
文摘A modified fractal growth model based on the deposition, diffusion, and aggregation (DDA) with cluster rotation is presented to simulate two-dimensional fractal aggregation on liquid surfaces. The mobility (including diffusion and rotation) of clusters is related to its mass, which is given by Dm = D0s-γD and θm = θ0s-γθ, respectively. We concentrate on revealing the details of the influence of deposition flux F, cluster diffusion factor γD and cluster rotation factor γθ on the dynamics of fractal aggregation on liquid surfaces. It is shown that the morphologies of clusters and values of cluster density and fractal dimension depend dramatically on the deposition flux and migration factors of clusters.
