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Digital media forensics can exploit the electric network frequency of audio signals to detect tampering. However, current electric network based audio forensic schemes are limited by their inability to obtain concurrent electric n...
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Digital media forensics can exploit the electric network frequency of audio signals to detect tampering. However, current electric network based audio forensic schemes are limited by their inability to obtain concurrent electric network frequency reference datasets from power grids. In addition, most forensic algorithms do not provide high detection precision in adverse signal-to-noise conditions. This chapter proposes an automated electric network frequency based audio forensic scheme that monitors abrupt mutations of tampered frames and discontinuities in the variations of electric network frequency features. Specifically, the scheme utilizes the multiple signal classification, Hilbert linear prediction and Welch algorithms to extract electric network frequency features from audio signals; the extracted features are passed to a convolutional neural network classifier to detect audio tampering. The negative effects of low signal-to-noise ratios on electric network frequency extraction are addressed by employing extra low-rank filtering that removes voice activity and noise interference. Simulation results demonstrate that the proposed scheme provides better audio tampering detection accuracy compared with a benchmark method, especially under adverse signal-to-noise conditions.
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摘要 :
Digital media forensics can exploit the electric network frequency of audio signals to detect tampering. However, current electric network based audio forensic schemes are limited by their inability to obtain concurrent electric n...
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Digital media forensics can exploit the electric network frequency of audio signals to detect tampering. However, current electric network based audio forensic schemes are limited by their inability to obtain concurrent electric network frequency reference datasets from power grids. In addition, most forensic algorithms do not provide high detection precision in adverse signal-to-noise conditions. This chapter proposes an automated electric network frequency based audio forensic scheme that monitors abrupt mutations of tampered frames and discontinuities in the variations of electric network frequency features. Specifically, the scheme utilizes the multiple signal classification, Hilbert linear prediction and Welch algorithms to extract electric network frequency features from audio signals; the extracted features are passed to a convolutional neural network classifier to detect audio tampering. The negative effects of low signal-to-noise ratios on electric network frequency extraction are addressed by employing extra low-rank filtering that removes voice activity and noise interference. Simulation results demonstrate that the proposed scheme provides better audio tampering detection accuracy compared with a benchmark method, especially under adverse signal-to-noise conditions.
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Automatic Check-Out (ACO) aims to accurately predict the presence and count of each category of products in check-out images, where a major challenge is the significant domain gap between training data (single-product exemplars) a...
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Automatic Check-Out (ACO) aims to accurately predict the presence and count of each category of products in check-out images, where a major challenge is the significant domain gap between training data (single-product exemplars) and test data (check-out images). To mitigate the gap, we propose a method, termed as PSP, to perform Prototype-based classifier learning from Single-Product exemplars. In PSP, by revealing the advantages of representing category semantics, the prototype representation of each product category is firstly obtained from single-product exemplars. Based on the prototypes, it then generates categorical classifiers with a background classifier to not only recognize finegrained product categories but also distinguish background upon product proposals derived from check-out images. To further improve the ACO accuracy, we develop discriminative re-ranking to both adjust the predicted scores of product proposals for bringing more discriminative ability in classifier learning and provide a reasonable sorting possibility by considering the fine-grained nature. Moreover, a multi-label recognition loss is also equipped for modeling co-occurrence of products in check-out images. Experiments are conducted on the large-scale RPC dataset for evaluations. Our ACO result achieves 86.69%, by 6.18% improvements over state-of-the-arts, which demonstrates the superiority of PSP.
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Coded Aperture Snapshot Spectral Imaging (CASSI) utilizes a two-dimensional (2D) detector to capture three-dimensional (3D) data, significantly reducing the acquisition cost of hyperspectral images. However, such an ill-posed prob...
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Coded Aperture Snapshot Spectral Imaging (CASSI) utilizes a two-dimensional (2D) detector to capture three-dimensional (3D) data, significantly reducing the acquisition cost of hyperspectral images. However, such an ill-posed problem desires a reliable decoding algorithm with a well-designed prior term. This paper proposes a decoding model with a learnable prior term for snapshot compressive imaging. We expand the inference obtained by Half Quadratic Splitting (HQS) to construct our Texture Enhancement Prior learning network, TEP-net. Considering the high-frequency information representing the texture can effectively enhance the reconstruction quality. We then propose the residual Shuffled Multi-spectral Channel Attention (Shuffled-MCA) module to learn information corresponding to different frequency components by introducing the Discrete Cosine Transform (DCT) bases. In order to overcome the drawbacks of grouping operations within the MCA module efficiently, we employ the channel shuffle operation instead of a channel-wise operation. Channel shuffle rearranges the channel descriptors, allowing for better extraction of channel correlations subsequently. The experimental results show that our method outperforms the existing state-of-the-art method in numerical indicators. At the same time, the visualization results also show our superior performance in texture enhancement.
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In this paper, we propose a fused extreme learning machine (ELM) method with multiview learning for hyperspectral imagery. The proposed approach consists of the following aspects. First, multiple views of spectral-spatial features...
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In this paper, we propose a fused extreme learning machine (ELM) method with multiview learning for hyperspectral imagery. The proposed approach consists of the following aspects. First, multiple views of spectral-spatial features are generated from the hyperspectral image by using a multiscale spectral-spatial context aware propagation filter. We next apply the weighted-based probabilistic ELM to these multiple feature views to obtain a robust supervised classification results with high accuracy. The advantages of the proposed method are twofold: (1) the multiscale local spectral-spatial contexts of the image are able to be exploited to improve the classification performance significantly; and (2) the algorithm is simple but very robust to the small size of training labeled samples. The experimental results suggest that the proposed algorithm obtains a competitive performance and outperforms other state-of-the-art ELM-based classifiers and the classical SVM classifier.
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In this paper, we propose a fused extreme learning machine (ELM) method with multiview learning for hyperspectral imagery. The proposed approach consists of the following aspects. First, multiple views of spectral-spatial features...
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In this paper, we propose a fused extreme learning machine (ELM) method with multiview learning for hyperspectral imagery. The proposed approach consists of the following aspects. First, multiple views of spectral-spatial features are generated from the hyperspectral image by using a multiscale spectral-spatial context aware propagation filter. We next apply the weighted-based probabilistic ELM to these multiple feature views to obtain a robust supervised classification results with high accuracy. The advantages of the proposed method are twofold: (1) the multiscale local spectral-spatial contexts of the image are able to be exploited to improve the classification performance significantly; and (2) the algorithm is simple but very robust to the small size of training labeled samples. The experimental results suggest that the proposed algorithm obtains a competitive performance and outperforms other state-of-the-art ELM-based classifiers and the classical SVM classifier.
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In order to adapt to the construction of the new generation power system, it is urgent to make use of various controllable resources to build a new generation of comprehensive defense system of power grid security. In this paper, ...
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In order to adapt to the construction of the new generation power system, it is urgent to make use of various controllable resources to build a new generation of comprehensive defense system of power grid security. In this paper, we aim to design a multi-objective dual-route planning algorithm for grid communication network. Firstly, we consider the network structure, site level and service load in the node and link risk model. On this basis, we calculate the overall risk equilibrium value of the network. Moreover, we also take into account the QoS performance to formulate the highly reliable dual-route planning problem with the optimization target of the network overall risk equilibrium value and the end-to-end communication delay. Then, we propose the multi-objective dual-route planning optimization algorithm to solve the problem. Simulation results show that the proposed algorithm has advance in terms of the balance of network risk and the performance of QoS.
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Distributed decision support systems designed for healthcare use can benefit from services and information available across a decentralised environment. The sophisticated nature of collaboration among involved partners who contrib...
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Distributed decision support systems designed for healthcare use can benefit from services and information available across a decentralised environment. The sophisticated nature of collaboration among involved partners who contribute services or sensitive data in this paradigm, however, demands careful attention from the beginning of designing such systems. Apart from the traditional need of secure data transmission across clinical centres, a more important issue arises from the need of consensus for access to system-wide resources by separately managed user groups from each centre. A primary concern is the determination of interactive tasks that should be made available to authorised users, and further the clinical resources that can be populated into interactions in compliance with user clinical roles and policies. To this end, explicit interaction modelling is put forward along with the contextual constraints within interactions that together enforce secure access, the interaction participation being governed by system-wide policies and local resource access being governed by node-wide policies. Clinical security requirements are comprehensively analysed, prior to the design and building of our security model. The application of the approach results in a Multi-Agent System driven by secure interaction models. This is illustrated using a prototype of the HealthAgents system.
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摘要 :
Distributed decision support systems designed for healthcare use can benefit from services and information available across a decentralised environment. The sophisticated nature of collaboration among involved partners who contrib...
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Distributed decision support systems designed for healthcare use can benefit from services and information available across a decentralised environment. The sophisticated nature of collaboration among involved partners who contribute services or sensitive data in this paradigm, however, demands careful attention from the beginning of designing such systems. Apart from the traditional need of secure data transmission across clinical centres, a more important issue arises from the need of consensus for access to system-wide resources by separately managed user groups from each centre. A primary concern is the determination of interactive tasks that should be made available to authorised users, and further the clinical resources that can be populated into interactions in compliance with user clinical roles and policies. To this end, explicit interaction modelling is put forward along with the contextual constraints within interactions that together enforce secure access, the interaction participation being governed by system-wide policies and local resource access being governed by node-wide policies. Clinical security requirements are comprehensively analysed, prior to the design and building of our security model. The application of the approach results in a Multi-Agent System driven by secure interaction models. This is illustrated using a prototype of the HealthAgents system.
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