32302991
Image coding by folding
We propose an image coding algorithm which employs data hiding techniques to "fold" an image into itself. Data hiding is a process of encoding extra information into a host image by making small modifications to its pixels. In our approach, an image is split into two parts of equal size: a host image and a residual image. The residual image is compressed into a bit stream and then embedded into the host image. The host image, which is 50% of the size of the original image, is coded using standard compression techniques. The embedded data does not increase the bit rate of the coded image. As a result, one may code only 50% pixels of the original image and still have perfect reconstruction. Experimental results indicate that the algorithm has a lot of potential in image coding.
1450773
Multiresolution video watermarking using perceptual models and scene segmentation
We introduce a watermarking procedure to embed copyright protection into digital video. Our video dependent watermarking procedure directly exploits the masking and temporal properties to embed an invisible and robust watermark. The watermark consists of static and dynamic temporal components that are generated from a temporal wavelet transform of the video scenes. To generate the watermark, the resulting wavelet coefficient frames are modified by a perceptually shaped pseudo-random sequence representing the author. The noise-like watermark is statistically undetectable to thwart unauthorized removal. Furthermore, the author representation resolves the deadlock problem. The multiresolution watermark may be detected on single frames without knowledge of the location of the frames in the video scene. We demonstrate the robustness of the watermarking procedure to several video distortions.
5048240
Current state of the art, challenges and future directions for audio watermarking
Transparent data embedding algorithms embed binary streams in host multimedia signals. The embedded data is perceptually inaudible to maintain the quality of the source data. The embedded data can add features to the host multimedia signal or provide copyright protection. We review developments and requirements in transparent data embedding techniques for audio signals. We describe our latest audio embedding algorithm and include experimental results indicating robustness to low bit rate MPEG-Layer 3, AAC, and Dolby AC-3 coding. We conclude with a discussion of future research directions.
9382607
User-interface Design of Web Application Using Object-Z
A notation is presented for formally describing Web application’s design of a user-interface. The function of a Web application is given using formal specification language Object-Z and then some generic classes, relationships and system actions are defined to specify user-interface design. In user-interface design, UAN annotations are added to Object-Z notation. The extended Object-Z notation expresses the presentation relationship between the design and the specification. This formal specification and design is useful in documenting a user-interface design and enables the analysis of the usability and safety of a Web application. In particular, it can be used to generate test cases automatically. An example of student information management system is used to illustrate the notation. Task efficiency is analyzed with the example.
15840871
Chaotic Simulated Annealing Neural Network with Decaying Chaotic Noise and Its Application in Economic Load Dispatch of Power Systems
Based on chaotic neural network (CNN), chaotic simulated annealing model with decaying chaotic noise (CSA-DCN) is presented. This model combines some advantages of Hopfield neural network (HNN) and simulated annealing (SA) algorithm, and decaying chaotic noise produced by iterated functions of logistic map is inducted into this model, which means it can be used to solve many multidimensioned, discrete, nonconvex, nonlinear constrained optimization problems, such as economic load dispatch (ELD) of power systems. Involved the transmission loss and valve point effect (VPE), the CSA-DCN model is applied to solve the ELD problem, the simulation results of three examples show that the CSA-DCN model for the ELD problem is versatile, robust and efficient.
15174288
Create a large-scale digital library for geo-referenced information
One of the most challenging research issues for geospatial collections is to develop techniques to support fuzzy, conceptbased, geographic information retrieval (GIR). Based on an artificial intelligence approach, this project presents a Geospatial Knowledge Representation System (GKRS) prototype system that integrates multiple multimedia (textual and image) knowledge sources to support concept-based GIR. Based on semantic network and neural network representations, GKRS loosely couples different knowledge sources and adopts spreading activation algorithms for concept-based knowledge inferencing. Both textual analysis and image processing techniques have been employed to create textual and visual geographical knowledge structures. This project suggests a framework for developing a complete GKRS-based system and describes in detail the prototype.
4515160
Alert Correlation for Extracting Attack Strategies
Alert correlation is an important technique for managing large the volume of intrusion alerts that are raised by heterogenous Intrusion Detection Systems (IDSs). The recent trend of research in this area is towards extracting attack strategies from raw intrusion alerts. It is generally believed that pure intrusion detection no longer can satisfy the security needs of organizations. Intrusion response and prevention are now becoming crucially important for protecting the network and minimizing damage. Knowing the real security situation of a network and the strategies used by the attackers enables network administrators to launches appropriate response to stop attacks and prevent them from escalating. This is also the primary goal of using alert correlation technique. However, most of the current alert correlation techniques only focus on clustering inter-connected alerts into different groups without further analyzing the strategies of the attackers. Some techniques for extracting attack strategies have been proposed in recent years, but they normally require defining a larger number of rules. This paper focuses on developing a new alert correlation technique that can help to automatically extract attack strategies from a large volume of intrusion alerts, without specific prior knowledge about these alerts. The proposed approach is based on two different neural network approaches, namely, Multilayer Perceptron (MLP) and Support Vector Machine (SVM). The probabilistic output of these two methods is used to determine with which previous alerts this current alert should be correlated. This suggests the causal relationship of two alerts, which is helpful for constructing attack scenarios. One of the distinguishing feature of the proposed technique is that an Alert Correlation Matrix (ACM) is used to store correlation strengthes of any two types of alerts. ACM is updated in the training process, and the information (correlation strength) is then used for extracting high level attack strategies.
18103320
Image Completion from Low-Level Learning
We present a learning-based approach to complete the missing parts of an image. Besides the conventional adopted image continuity and coherency heuristics, learnt image patches are used to better regularize the completion result. Through the learning process from a collection of commonly encountered natural images, we built a synthetic world consisting of scenes and their corresponding images. We further model the inter-patch relationships with a Markov Network. A belief propagation scheme is then used to choose and update a latent scene structure based on a maximal posterior probability estimation of the given image. The above operation usually converges within a few iterations. The obtained image is visually realistic.