2190827
Chosen-Plaintext Cryptanalysis of a Clipped-Neural-Network-Based Chaotic Cipher
At ISNN'04, a novel symmetric cipher was proposed, by combining a chaotic signal and a clipped neural network (CNN) for encryption. The present paper analyzes the security of this chaotic cipher against chosen-plaintext attacks, and points out that this cipher can be broken by a chosen-plaintext attack. Experimental analyses are given to support the feasibility of the proposed attack.
2501834
On the security of the Yen-Guo's domino signal encryption algorithm (DSEA)
Recently, a new domino signal encryption algorithm (DSEA) was proposed for digital signal transmission, especially for digital images and videos. This paper analyzes the security of DSEA, and points out the following weaknesses: (1) its security against the brute-force attack was overestimated; (2) it is not sufficiently secure against ciphertext-only attacks, and only one ciphertext is enough to get some information about the plaintext and to break the value of a sub-key; (3) it is insecure against known/chosen-plaintext attacks, in the sense that the secret key can be recovered from a number of continuous bytes of only one known/chosen plaintext and the corresponding ciphertext. Experimental results are given to show the performance of the proposed attacks, and some countermeasures are discussed to improve DSEA.
3106412
On the Design of Perceptual MPEG-Video Encryption Algorithms
In this paper, some existing perceptual encryption algorithms of MPEG videos are reviewed and some problems, especially security defects of two recently proposed MPEG-video perceptual encryption schemes, are pointed out. Then, a simpler and more effective design is suggested, which selectively encrypts fixed-length codewords in MPEG-video bit streams under the control of three perceptibility factors. The proposed design is actually an encryption configuration that can work with any stream cipher or block cipher. Compared with the previously-proposed schemes, the new design provides more useful features, such as strict size-preservation, on-the-fly encryption and multiple perceptibility, which make it possible to support more applications with different requirements. In addition, four different measures are suggested to provide better security against known/chosen-plaintext attacks
14536606
On the security of an image encryption method
The security of digital images has attracted much attention, and many different image encryption methods have been proposed. Yen and Guo (see Proc. IEEE Workshop Signal Processing Systems, p.430-37, 1999) proposed a novel image encryption algorithm called BRIE (bit recirculation image encryption). This paper points out that BRIE is not secure enough from strict cryptographic viewpoint. It has been found that some defects exist in BRIE, and a known/chosen-plaintext attack can break BRIE with only one known/chosen plain-image. Experiments are made to verify the defects of BRIE and the feasibility of the attack.
676909
Real-time multiple objects tracking with occlusion handling in dynamic scenes
This work presents a real-time system for multiple objects tracking in dynamic scenes. A unique characteristic of the system is its ability to cope with long-duration and complete occlusion without a prior knowledge about the shape or motion of objects. The system produces good segment and tracking results at a frame rate of 15-20 fps for image size of 320 /spl times/ 240, as demonstrated by extensive experiments performed using video sequences under different conditions indoor and outdoor with long-duration and complete occlusions in changing background.
10218510
Neural Network Based Feedback Scheduler for Networked Control System with Flexible Workload
Most control applications closed over a shared network are suffering from the time-varying characteristics of flexible network workload. This gives rise to non-deterministic availability of communication resources and may significantly impact the control performance. In the context of integrating control and scheduling, a novel feedback scheduler based on neural networks is suggested. With a modular architecture, the proposed feedback scheduler mainly consists of a monitor, a predictor, a regulator and an actuator. An online learning Elman neural network is employed to predict the network conditions, and then the control period is dynamically adjusted in response to estimated available network utilization. A fast algorithm for period regulation is employed. Preliminary simulation results show that the proposed feedback scheduler is effective in managing workload variations and can provide runtime flexibility to networked control applications.
45808479
Recursive Construction of Distributors by Merging
20338082
Guaranteed Cost Control of Networked Control Systems: An LMI Approach
In this paper, networked control systems (NCS) is modeled as a discrete-time linear state-delayed system with norm-bounded uncertainty. Inspired by the so-called descriptor model transformation, a delay-dependent sufficient condition for the existence of a guaranteed cost controller for NCS is presented by a set of linear matrix inequalities (LMIs). The resulting controller can not only asymptotically stabilize the system but also guarantee an adequate level of performance. Theoretical analysis and illustrative results show that the control strategy presented in this paper is effective and feasible.
46006955
Improved algorithm for finding next-to-shortest paths
We study the problem of finding the next-to-shortest paths in a weighted undirected graph. A next-to-shortest (u, v)-path is a shortest (u, v)-path amongst (u, v)-paths with length strictly greater than the length of the shortest (u, v)-path. The first polynomial algorithm for this problem was presented in [I. Krasikov, S.D. Noble, Finding next-to-shortest paths in a graph, Inform. Process. Lett. 92 (2004) 117-119]. We improve the upper bound from O(n3m) to O(n3).
14555654
Face recognition by support vector machines
Support vector machines (SVM) have been recently proposed as a new technique for pattern recognition. SVM with a binary tree recognition strategy are used to tackle the face recognition problem. We illustrate the potential of SVM on the Cambridge ORL face database, which consists of 400 images of 40 individuals, containing quite a high degree of variability in expression, pose, and facial details. We also present the recognition experiment on a larger face database of 1079 images of 137 individuals. We compare the SVM-based recognition with the standard eigenface approach using the nearest center classification (NCC) criterion.