439 research outputs found
Robust Image Hashing Based Efficient Authentication for Smart Industrial Environment
[EN] Due to large volume and high variability of editing tools, protecting multimedia contents, and ensuring their privacy and authenticity has become an increasingly important issue in cyber-physical security of industrial environments, especially industrial surveillance. The approaches authenticating images using their principle content emerge as popular authentication techniques in industrial video surveillance applications. But maintaining a good tradeoff between perceptual robustness and discriminations is the key research challenge in image hashing approaches. In this paper, a robust image hashing method is proposed for efficient authentication of keyframes extracted from surveillance video data. A novel feature extraction strategy is employed in the proposed image hashing approach for authentication by extracting two important features: the positions of rich and nonzero low edge blocks and the dominant discrete cosine transform (DCT) coefficients of the corresponding rich edge blocks, keeping the computational cost at minimum. Extensive experiments conducted from different perspectives suggest that the proposed approach provides a trustworthy and secure way of multimedia data transmission over surveillance networks. Further, the results vindicate the suitability of our proposal for real-time authentication and embedded security in smart industrial applications compared to state-of-the-art methods.This work was supported in part by the National Natural Science Foundation of China under Grant 61976120, in part by the Natural Science Foundation of Jiangsu Province under Grant BK20191445, in part by the Six Talent Peaks Project of Jiangsu Province under Grant XYDXXJS-048, and sponsored by Qing Lan Project of Jiangsu Province, China.Sajjad, M.; Ul Haq, I.; Lloret, J.; Ding, W.; Muhammad, K. (2019). Robust Image Hashing Based Efficient Authentication for Smart Industrial Environment. IEEE Transactions on Industrial Informatics. 15(12):6541-6550. https://doi.org/10.1109/TII.2019.2921652S65416550151
InfoFlowNet: A Multi-head Attention-based Self-supervised Learning Model with Surrogate Approach for Uncovering Brain Effective Connectivity
Deciphering brain network topology can enhance the depth of neuroscientific
knowledge and facilitate the development of neural engineering methods.
Effective connectivity, a measure of brain network dynamics, is particularly
useful for investigating the directional influences among different brain
regions. In this study, we introduce a novel brain causal inference model named
InfoFlowNet, which leverages the self-attention mechanism to capture
associations among electroencephalogram (EEG) time series. The proposed method
estimates the magnitude of directional information flow (dIF) among EEG
processes by measuring the loss of model inference resulting from the shuffling
of the time order of the original time series. To evaluate the feasibility of
InfoFlowNet, we conducted experiments using a synthetic time series and two EEG
datasets. The results demonstrate that InfoFlowNet can extract time-varying
causal relationships among processes, reflected in the fluctuation of dIF
values. Compared with the Granger causality model and temporal causal discovery
framework, InfoFlowNet can identify more significant causal edges underlying
EEG processes while maintaining an acceptable computation time. Our work
demonstrates the potential of InfoFlowNet for analyzing effective connectivity
in EEG data. The findings highlight the importance of effective connectivity in
understanding the complex dynamics of the brain network
- …