水印鲁棒性:纠错编码有效吗?

Robustness is one of the most important requirements of digital watermarking for many applications. Spread-spectrum based methods are not effective enough to ensure their robustness. By modeling digital watermarking as digital communications, several researchers proposed using error correcting coding (ECC) to improve robustness. However, an important fact that has long been neglected is that due to the imperceptibility requirement, the redundancy introduced by ECC will lead to a decrease of the magnitude of watermark signal. Therefore, a problem arises naturally: Could the usage of ECC effectively improve the robustness of watermarking? This paper addresses this problem from the perspectives of both theoretical analysis and experimental investigation. Our investigation shows that ECC cannot effectively improve the robustness of watermarking against a great majority of various attacks except for cropping and jitter attacks. Hence, ECC should not be considered as a universal measure that can be employed to enhance robustness of watermarking.     

Non-ambiguity of blind watermarking: a revisit with analytical resolution

Resistance to ambiguity attack is an important requirement for a secure digital rights management (DRM) system. In this paper,we revisit the non-ambiguity of a blind watermarking based on the compu-tational indistinguishability between pseudo random sequence generator (PRSG) sequence ensemble and truly random sequence ensemble. Ambiguity attacker on a watermarking scheme,which uses a PRSG sequence as watermark,is viewed as an attacker who tries to attack a noisy PRSG sequence. We propose and prove the security theorem for binary noisy PRSG sequence and security theorem for gen-eral noisy PRSG sequence. It is shown that with the proper choice of the detection threshold Th = an1/2 (a is a normalized detection threshold; n is the length of a PRSG sequence) and n 1.39×m/a2 (m is the key length),the success probability of an ambiguity attack and the missed detection probability can both be made negligibly small thus non-ambiguity and robustness can be achieved simultaneously for both practical quantization-based and blind spread spectrum (SS) watermarking schemes. These analytical resolutions may be used in designing practical non-invertible watermarking schemes and measuring the non-ambiguity of the schemes.     

A self-recovery fragile watermarking scheme for image authentication with superior localization

To address the problems of the inferior localization and high probability of false rejection in existing self-recovery fragile watermarking algorithms, this paper proposes a new self-recovery fragile watermarking scheme with superior localization, and further discusses the probability of false rejection （PFR） and the probability of false acceptance （PFA） of the proposed scheme. Moreover, four measurements are defined to evaluate the quality of a recovered image. In the proposed algorithm, the original image is divided into 2×2 blocks to improve localization precision and decrease PFR under occurrence of random tampering. The PFR under occurrence of region tampering can be effectively decreased by randomly embedding the watermark of each block in conjunction with a novel method of tamper detection. Compared with the current self-recovery fragile watermarking algorithms, the proposed scheme not only resolves the tamper detection problem of self-embedding watermarking, but also improves the robustness against the random tampering of self-embedding watermarking. In addition, the subjective measurements are provided to evaluate the performance of the self-recovery watermarking schemes for image authentication.     

RST transforms resistant image watermarking based on centroid and sector-shaped partition

A novel digital watermarking scheme featuring centroid-based sectoring is proposed in this paper. To get higher robustness against geometric attacks, such as rotation, scaling, and translation (RST), a delicate synchronization mechanism was developed and incorporated into the proposed approach. During the process of watermark embedding, the original image was partitioned into sectors based on the image centroid. Synchronization information as well as the message bits is then embedded into these sectors. With the help of the centroid-based sectoring and synchronization information, the proposed approach is capable of restoring the correct sectoring even if it has experienced severe geometric distortion. This attribute ensures the correct recovery of embedded watermarks and contributes to the robustness of the proposed scheme. A series of experiments have been conducted to verify the feasibility and effectiveness of the proposed approach. Experimental results show that the proposed scheme possesses good robustness against RST attacks and considerable robustness against other common image processing attacks.     

A fast audio digital watermark method based on counter-propagation neural networks

In this thesis, we present a novel audio digital watermark method based on counter-propagation neural networks. After dealing with the audio by discrete wavelet transform, we select the important coefficients which are ready to be trained in the neural networks. By making use of the capabilities of memorization and fault tolerance in CPN, watermark is memorized in the nerve cells of CPN. In addition, we adopt a kind of architecture with an adaptive number of parallel CPN to treat with each audio frame and the corresponding watermark bit. Comparing with other traditional methods by using CPN, it was largely improve the efficiency for watermark embedding and correctness for extracting, namely the speed of whole algorithm. The extensive experimental results show that, we can detect the watermark exactly under most of attacks. This method tradeoff both the robustness and inaudibility of the audio digital watermark efficaciously.     

A robust watermarking algorithm based on QR factorization and DCT using quantization index modulation technique

We propose a robust digital watermarking algorithm for copyright protection.A stable feature is obtained by utilizing QR factorization and discrete cosine transform(DCT) techniques,and a meaningful watermark image is embedded into an image by modifying the stable feature with a quantization index modulation(QIM) method.The combination of QR factorization,DCT,and QIM techniques guarantees the robustness of the algorithm.Furthermore,an embedding location selection method is exploited to select blocks with small modifications as the embedding locations.This can minimize the embedding distortion and greatly improve the imperceptibility of our scheme.Several standard images were tested and the experimental results were compared with those of other published schemes.The results demonstrate that our proposed scheme can achieve not only better imperceptibility,but also stronger robustness against common signal processing operations and lossy compressions,such as filtering,noise addition,scaling,sharpening,rotation,cropping,and JPEG/JPEG2000 compression.     

A blind watermarking algorithm for 3D mesh models based on vertex curvature

We propose a robust blind watermarking algorithm for three-dimensional （3D） mesh models based on vertex cur-vature to maintain good robustness and improve visual masking in 3D mesh models. In the embedding process, by using the local window of vertex, the root mean square curvature is calculated for every vertex of the 3D mesh model and an ordered set of fluctuation values is obtained. According to the ordered fluctuation values, the vertices are separated into bins. In each bin the fluctuation values are normalized. Finally, the mean of the root mean square curvature fluctuation values of the vertices in each bin is modulated to embed watermark information. In watermark detection, the algorithm uses a blind watermark extraction technique to extract the watermark information. The experimental results show that the algorithm has a very good performance for visual masking of the embedded model and that it can resist a variety of common attacks such as vertex rearrangement, rotation, trans-lating, uniform scaling, noise, smoothing, quantization, and simplification.     

A New Content-Based Digital Audio Watermarking Algorithm for Copyright Protection

Digital audio watermarking embeds inaudible information into digital audio data for the purposes of copyright protection, ownership verification, coven communication, and/or auxiliary data carrying. In this paper, we present a novel watermarking scheme to embed a meaningful gray image into digital audio by quantizing the wavelet coefficients （using integer lifting wavelet transform） of audio samples. Our audiodependent watermarking procedure directly exploits temporal and frequency perceptual masking of the human auditory system （HAS） to guarantee that the embedded watermark image is inaudible and robust. The watermark is constructed by utilizing still image compression technique, breaking each audio clip into smaller segments, selecting the perceptually significant audio segments to wavelet transform, and quantizing the perceptually significant wavelet coefficients. The proposed watermarking algorithm can extract the watermark image without the help from the original digital audio signals. We also demonstrate the robustness of that watermarking procedure to audio degradations and distortions, e.g., those that result from noise adding, MPEG compression, low pass filtering, resampling, and requantization.     

Energy-efficient power allocation for selfish cooperative communication networks using bargaining game

In commercial networks,user nodes operating on batteries are assumed to be selfish to consume their energy solely to maximize their own benefits,e.g.,data rates.In this paper,we propose a bargaining game to perform the power allocation for the selfish cooperative communication networks.In our system,two partner nodes can act as a source as well as a relay for each other,and each node is with an energy constraint to transmit one frame.Consider a selfish node is willing to seek cooperative transmission only if the data rate achieved through cooperation will not lower than that achieved through noncooperation by using the same amount of energy.The energy-efficient power allocation problem can be modeled as a cooperative game.We proved that there exists a unique Nash bargaining solution (NBS) for the game by verifying that the game is indeed a bargaining problem.Thus,the two objectives,i.e.,system efficiency and user fairness specified in the selfish networks can be achieved.Simulation results show that the NBS scheme is efficient in that the performance loss of the NBS scheme to that of the maximal overall rate scheme is small while the maximal-rate scheme is unfair.The simulation results also show that the NBS result is fair in that both nodes could experience better performance than they work independently and the degree of cooperation of a node only depends on how much contribution its partner can make to improve its own performance.     

Digital Image Watermarking Based on Discrete Wavelet Transform

This paper aims at digital watermark which is a new popular research topic recently,presents some methods to embed digital watermark based on modifying frequency coefficients in discrete wavelet transform(DWT) domian,Fist,the,the present progress of digital watermark is briefly introduced;after that,starting from Pitas‘s method and discarding his pseudo random number method,the authors use a digital image scrambling technology as preprocessing for digital watermarking ,Then the authors discuss how to embed a 1-bit digital image as watermark in frequency domain.Finally another digital watermarking method is given in which3-D DWT is used to transform a given digtial image .Based on the experimental results ,it is shown that the proposed methods are robust to a large extent.     

Software Defect Detection with R<Emphasis Type="SmallCaps">ocus</Emphasis>

Software defect detection aims to automatically identify defective software modules for efficient software test in order to improve the quality of a software system.Although many machine learning methods have been successfully applied to the task,most of them fail to consider two practical yet important issues in software defect detection.First,it is rather difficult to collect a large amount of labeled training data for learning a well-performing model;second,in a software system there are usually much fewer defective modules than defect-free modules,so learning would have to be conducted over an imbalanced data set.In this paper,we address these two practical issues simultaneously by proposing a novel semi-supervised learning approach named Rocus.This method exploits the abundant unlabeled examples to improve the detection accuracy,as well as employs under-sampling to tackle the class-imbalance problem in the learning process.Experimental results of real-world software defect detection tasks show that Rocus is effective for software defect detection.Its performance is better than a semi-supervised learning method that ignores the class-imbalance nature of the task and a class-imbalance learning method that does not make effective use of unlabeled data.     

HAPE3D—a new constructive algorithm for the 3D irregular packing problem

We propose a new constructive algorithm, called HAPE3 D, which is a heuristic algorithm based on the principle of minimum total potential energy for the 3D irregular packing problem, involving packing a set of irregularly shaped polyhedrons into a box-shaped container with fixed width and length but unconstrained height. The objective is to allocate all the polyhedrons in the container, and thus minimize the waste or maximize profit. HAPE3 D can deal with arbitrarily shaped polyhedrons, which can be rotated around each coordinate axis at different angles. The most outstanding merit is that HAPE3 D does not need to calculate no-fit polyhedron(NFP), which is a huge obstacle for the 3D packing problem. HAPE3 D can also be hybridized with a meta-heuristic algorithm such as simulated annealing. Two groups of computational experiments demonstrate the good performance of HAPE3 D and prove that it can be hybridized quite well with a meta-heuristic algorithm to further improve the packing quality.     

Log-scaling magnitude modulated watermarking scheme

A real-time watermarking scheme with high robustness and security has been proposed based on modulating the log-scaling magnitudes of DCT coefficients, which is most suitable for JPEG images and MPEG streams. The watermark bit is encoded as the sign of the difference between the individual log-scaling magnitude of a group-region and the average one of all group-regions. The log-scaling magnitude can be modulated by modifying the low and middle frequency DCT coefficients imperceptibly. The robustness of scheme is not only dependent on those largest coefficients, but also on the other coefficients with the same proportion. It can embed 512 bits into an image with a size of 512×512, which can satisfy the payload requirement of most video watermarking applications. Moreover, the watermark embedding process only requires one-sixth of the time consumed during normal playing of video, and the watermark detection only requires one-twelfth of that, which can meet the real-time requirements of most video watermarking applications. Furthermore, the experimental results show that the presented scheme is transparent and robust to significant valumetric distortions (including additive noise, low-pass filtering, lossy compression and valumetric scaling) and a part of geometric distortions. It performs much better than the EMW algorithm in resisting all kinds of distortions except Gaussian noise with a larger deviation.     

Generating test data for both path coverage and fault detection using genetic algorithms

The aim of software testing is to find faults in a program under test, so generating test data that can expose the faults of a program is very important. To date, current stud- ies on generating test data for path coverage do not perform well in detecting low probability faults on the covered path. The automatic generation of test data for both path coverage and fault detection using genetic algorithms is the focus of this study. To this end, the problem is first formulated as a bi-objective optimization problem with one constraint whose objectives are the number of faults detected in the traversed path and the risk level of these faults, and whose constraint is that the traversed path must be the target path. An evolution- ary algorithm is employed to solve the formulated model, and several types of fault detection methods are given. Finally, the proposed method is applied to several real-world programs, and compared with a random method and evolutionary opti- mization method in the following three aspects： the number of generations and the time consumption needed to generate desired test data, and the success rate of detecting faults. The experimental results confirm that the proposed method can effectively generate test data that not only traverse the target path but also detect faults lying in it.     

A unified algorithm for mobility load balancing in 3GPP LTE multi-cell networks

3GPP long term evolution (LTE) is a promising candidate for the next-generation wireless network, which is expected to achieve high spectrum efficiency by using advanced physical layer techniques and flat network structures. However, the LTE network still faces the problem of load imbalance as in GSM/WCDMA networks, and this may cause significant deterioration of system performance. To deal with this problem, mobility load balancing (MLB) has been proposed as an important use case in 3GPP self-organizing network (SON), in which the serving cell of a user can be selected to achieve load balancing rather than act as the cell with the maximum received power. Furthermore, the LTE network aims to serve users with different quality-of-service (QoS) requirements, and the network-wide objective function for load balancing is distinct for different kinds of users. Thus, in this paper, a unified algorithm is proposed for MLB in the LTE network. The load balancing problem is first formulated as an optimization problem with the optimizing variables being cell-user connections. Then the complexity and overhead of the optimal solution is analyzed and a practical and distributed algorithm is given. After that, the proposed algorithm is evaluated for users with different kinds of QoS requirements, i.e., guaranteed bit rate (GBR) users with the objective function of load balance index and non-GBR (nGBR) users with the objective function of total utility, respectively. Simulation results show that the proposed algorithm leads to significantly balanced load distribution for GBR users to decrease the new call blocking rate, and for nGBR users to improve the cell-edge throughput at the cost of only slight deterioration of total throughput.     

Correlation between Transport and Occurrence of Dengue Cases in Bahia

Dengue is a public health problem that presents complexity in its dissemination. The physical means of spreading and the dynamics of the spread between the municipalities need to be analyzed to guide effective public policies to combat this problem. This study shows a correlation between the exponent of criticality present in SOC （self-organized criticality） and the number of buses per week, identifying municipalities that exert important roles in the spread of dengue in Bahia, confirming transport as a physical means for the diffusion of dengue.