Low-Cost CNN for Automatic Violence Recognition on Embedded System

Vieira, Joelton Cezar; Sartori, Andreza; Stefenon, Stéfano Frizzo; Perez, Fábio; Jesus, Gabriel Schneider de; Leithardt, Valderi Reis Quietinho

doi:10.1109/access.2022.3155123

Cited by 41 publications

(20 citation statements)

References 53 publications

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“…CNN module mainly includes a convolution layer, normalization layer, RELU layer, pooling layer, etc., and extracts specific patterns [ 33 ] and hidden information in statistical feature through convolution units composed of different neural network layers, and finally output the expanded one-dimensional vector.…”

Section: Proposed Methodsmentioning

confidence: 99%

FedHGCDroid: An Adaptive Multi-Dimensional Federated Learning for Privacy-Preserving Android Malware Classification

Jiang

Yin

Xia

et al. 2022

Entropy

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With the popularity of Android and its open source, the Android platform has become an attractive target for hackers, and the detection and classification of malware has become a research hotspot. Existing malware classification methods rely on complex manual operation or large-volume high-quality training data. However, malware data collected by security providers contains user privacy information, such as user identity and behavior habit information. The increasing concern for user privacy poses a challenge to the current malware classification scheme. Based on this problem, we propose a new android malware classification scheme based on Federated learning, named FedHGCDroid, which classifies malware on Android clients in a privacy-protected manner. Firstly, we use a convolutional neural network and graph neural network to design a novel multi-dimensional malware classification model HGCDroid, which can effectively extract malicious behavior features to classify the malware accurately. Secondly, we introduce an FL framework to enable distributed Android clients to collaboratively train a comprehensive Android malware classification model in a privacy-preserving way. Finally, to adapt to the non-IID distribution of malware on Android clients, we propose a contribution degree-based adaptive classifier training mechanism FedAdapt to improve the adaptability of the malware classifier based on Federated learning. Comprehensive experimental studies on the Androzoo dataset (under different non-IID data settings) show that the FedHGCDroid achieves more adaptability and higher accuracy than the other state-of-the-art methods.

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Section: Proposed Methodsmentioning

confidence: 99%

FedHGCDroid: An Adaptive Multi-Dimensional Federated Learning for Privacy-Preserving Android Malware Classification

Jiang

Yin

Xia

et al. 2022

Entropy

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“…In [59], differently from the above methods, the socalled SMART approach leverages a multi-frame attention and relation network to select the most informative frames in short trimmed videos. In another line of research in the efficient video recognition paradigm, in [2], a low-cost CNN implemented in an embedded platform is used for violence recognition in video.…”

Section: ) Top-down Approachesmentioning

confidence: 99%

ViGAT: Bottom-Up Event Recognition and Explanation in Video Using Factorized Graph Attention Network

2022

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In this paper a pure-attention bottom-up approach, called ViGAT, that utilizes an object detector together with a Vision Transformer (ViT) backbone network to derive object and frame features, and a head network to process these features for the task of event recognition and explanation in video, is proposed. The ViGAT head consists of graph attention network (GAT) blocks factorized along the spatial and temporal dimensions in order to capture effectively both local and long-term dependencies between objects or frames. Moreover, using the weighted in-degrees (WiDs) derived from the adjacency matrices at the various GAT blocks, we show that the proposed architecture can identify the most salient objects and frames that explain the decision of the network. A comprehensive evaluation study is performed, demonstrating that the proposed approach provides state-of-the-art results on three large, publicly available video datasets (FCVID, MiniKinetics, ActivityNet) a .a Source code and trained models will be made available upon acceptance.INDEX TERMS Video event recognition, eXplainable AI (XAI), graph attention network, factorized attention, bottom-up.

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“…From the image processing of failed components, it is possible to identify patterns and thus improve their identification in the field [28]. Several researchers are using object detection and image classification based on convolutional neural networks (CNNs) models [29][30][31]. The CNNs can be specially applied to improve the ability to identify faulty components, as shown by Liu et al [32] and Sadykova et al [33] using You Only Look Once (YOLO), Li et al [34] with an improved Faster R-CNN, and Wen et al [35] using Exact R-CNN.…”

Section: Related Workmentioning

confidence: 99%

Wavelet Long Short-Term Memory to Fault Forecasting in Electrical Power Grids

Branco¹,

Cavalca²,

Stefenon³

et al. 2022

Preprint

Self Cite

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The electric power distribution utility is responsible for providing energy to consumers in a continuous and stable way, failures in the electrical power system reduce the reliability indexes of the grid, directly harming its performance. For this reason, there is a need for failure prediction to reestablish power in the shortest possible time. Considering an evaluation of the number of failures over time, this paper proposes to perform a failure prediction during the first year of the pandemic in Brazil (2020) to verify the feasibility of using time series forecasting models for fault prediction. The Long Short-Term Memory (LSTM) model will be evaluated to obtain a forecast result that can be used by the electric power utility to organize the maintenance teams. The Wavelet transform shows to be promising in improving the predictive ability of the LSTM, making the Wavelet LSTM model suitable for the study at hand. The results show that the proposed approach has better results regarding the evaluation of the error in prediction and has robustness when a statistical analysis is performed.

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Low-Cost CNN for Automatic Violence Recognition on Embedded System

Cited by 41 publications

References 53 publications

FedHGCDroid: An Adaptive Multi-Dimensional Federated Learning for Privacy-Preserving Android Malware Classification

FedHGCDroid: An Adaptive Multi-Dimensional Federated Learning for Privacy-Preserving Android Malware Classification

ViGAT: Bottom-Up Event Recognition and Explanation in Video Using Factorized Graph Attention Network

Wavelet Long Short-Term Memory to Fault Forecasting in Electrical Power Grids

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