Two-Stage Violence Detection Using ViTPose and Classification Models at Smart Airports

• URL: http://arxiv.org/abs/2308.16325v1
• Date: Wed, 30 Aug 2023 21:20:15 GMT
• Title: Two-Stage Violence Detection Using ViTPose and Classification Models at Smart Airports
• Authors: \.Irem \"Ustek, Jay Desai, Iv\'an L\'opez Torrecillas, Sofiane Abadou, Jinjie Wang, Quentin Fever, Sandhya Rani Kasthuri, Yang Xing, Weisi Guo, Antonios Tsourdos
• Abstract summary: This study introduces an innovative violence detection framework tailored to the unique requirements of smart airports. The framework harnesses the power of ViTPose for human pose estimation. The solution underwent integrated testing to ensure robust performance in real world scenarios.
• Score: 9.53984191161849
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This study introduces an innovative violence detection framework tailored to the unique requirements of smart airports, where prompt responses to violent situations are crucial. The proposed framework harnesses the power of ViTPose for human pose estimation. It employs a CNN - BiLSTM network to analyse spatial and temporal information within keypoints sequences, enabling the accurate classification of violent behaviour in real time. Seamlessly integrated within the SAFE (Situational Awareness for Enhanced Security framework of SAAB, the solution underwent integrated testing to ensure robust performance in real world scenarios. The AIRTLab dataset, characterized by its high video quality and relevance to surveillance scenarios, is utilized in this study to enhance the model's accuracy and mitigate false positives. As airports face increased foot traffic in the post pandemic era, implementing AI driven violence detection systems, such as the one proposed, is paramount for improving security, expediting response times, and promoting data informed decision making. The implementation of this framework not only diminishes the probability of violent events but also assists surveillance teams in effectively addressing potential threats, ultimately fostering a more secure and protected aviation sector. Codes are available at: https://github.com/Asami-1/GDP.

Related papers

• Real-Time Weapon Detection Using YOLOv8 for Enhanced Safety [0.0]
  The model was trained on a comprehensive dataset containing thousands of images depicting various types of firearms and edged weapons. We evaluated the model's performance using key metrics such as precision, recall, F1-score, and mean Average Precision (mAP) across multiple Intersection over Union (IoU) thresholds.
  arXiv  Detail & Related papers  (2024-10-23T10:35:51Z)
• Enabling Privacy-Preserving Cyber Threat Detection with Federated Learning [4.475514208635884]
  This study systematically profiles the (in)feasibility of learning for privacy-preserving cyber threat detection in terms of effectiveness, byzantine resilience, and efficiency. It shows that FL-trained detection models can achieve a performance that is comparable to centrally trained counterparts. Under a realistic threat model, FL turns out to be adversary-resistant to attacks of both data poisoning and model poisoning.
  arXiv  Detail & Related papers  (2024-04-08T01:16:56Z)
• FaultGuard: A Generative Approach to Resilient Fault Prediction in Smart Electrical Grids [53.2306792009435]
  FaultGuard is the first framework for fault type and zone classification resilient to adversarial attacks. We propose a low-complexity fault prediction model and an online adversarial training technique to enhance robustness. Our model outclasses the state-of-the-art for resilient fault prediction benchmarking, with an accuracy of up to 0.958.
  arXiv  Detail & Related papers  (2024-03-26T08:51:23Z)
• Attention-GAN for Anomaly Detection: A Cutting-Edge Approach to Cybersecurity Threat Management [0.0]
  This paper proposes an innovative Attention-GAN framework for enhancing cybersecurity, focusing on anomaly detection. The proposed approach aims to generate diverse and realistic synthetic attack scenarios, thereby enriching the dataset and improving threat identification. Integrating attention mechanisms with Generative Adversarial Networks (GANs) is a key feature of the proposed method. The attention-GAN framework has emerged as a pioneering approach, setting a new benchmark for advanced cyber-defense strategies.
  arXiv  Detail & Related papers  (2024-02-25T01:10:55Z)
• Avoid Adversarial Adaption in Federated Learning by Multi-Metric Investigations [55.2480439325792]
  Federated Learning (FL) facilitates decentralized machine learning model training, preserving data privacy, lowering communication costs, and boosting model performance through diversified data sources. FL faces vulnerabilities such as poisoning attacks, undermining model integrity with both untargeted performance degradation and targeted backdoor attacks. We define a new notion of strong adaptive adversaries, capable of adapting to multiple objectives simultaneously. MESAS is the first defense robust against strong adaptive adversaries, effective in real-world data scenarios, with an average overhead of just 24.37 seconds.
  arXiv  Detail & Related papers  (2023-06-06T11:44:42Z)
• SSIVD-Net: A Novel Salient Super Image Classification & Detection Technique for Weaponized Violence [3.651114792588495]
  Detection of violence and weaponized violence in CCTV footage requires a comprehensive approach. We introduce the emphSmart-City CCTV Violence Detection (SCVD) dataset. We propose a novel technique called emphSSIVD-Net (textbfSalient-textbfSuper-textbfImage for textbfViolence textbfDetection)
  arXiv  Detail & Related papers  (2022-07-26T12:31:01Z)
• Fixed Points in Cyber Space: Rethinking Optimal Evasion Attacks in the Age of AI-NIDS [70.60975663021952]
  We study blackbox adversarial attacks on network classifiers. We argue that attacker-defender fixed points are themselves general-sum games with complex phase transitions. We show that a continual learning approach is required to study attacker-defender dynamics.
  arXiv  Detail & Related papers  (2021-11-23T23:42:16Z)
• Measurement-driven Security Analysis of Imperceptible Impersonation Attacks [54.727945432381716]
  We study the exploitability of Deep Neural Network-based Face Recognition systems. We show that factors such as skin color, gender, and age, impact the ability to carry out an attack on a specific target victim. We also study the feasibility of constructing universal attacks that are robust to different poses or views of the attacker's face.
  arXiv  Detail & Related papers  (2020-08-26T19:27:27Z)
• Bayesian Optimization with Machine Learning Algorithms Towards Anomaly Detection [66.05992706105224]
  In this paper, an effective anomaly detection framework is proposed utilizing Bayesian Optimization technique. The performance of the considered algorithms is evaluated using the ISCX 2012 dataset. Experimental results show the effectiveness of the proposed framework in term of accuracy rate, precision, low-false alarm rate, and recall.
  arXiv  Detail & Related papers  (2020-08-05T19:29:35Z)
• Active Fuzzing for Testing and Securing Cyber-Physical Systems [8.228859318969082]
  We propose active fuzzing, an automatic approach for finding test suites of packet-level CPS network attacks. Key to our solution is the use of online active learning, which iteratively updates the models by sampling payloads. We evaluate the efficacy of active fuzzing by implementing it for a water purification plant testbed, finding it can automatically discover a test suite of flow, pressure, and over/underflow attacks.
  arXiv  Detail & Related papers  (2020-05-28T16:19:50Z)
• Adversarial vs behavioural-based defensive AI with joint, continual and active learning: automated evaluation of robustness to deception, poisoning and concept drift [62.997667081978825]
  Recent advancements in Artificial Intelligence (AI) have brought new capabilities to behavioural analysis (UEBA) for cyber-security. In this paper, we present a solution to effectively mitigate this attack by improving the detection process and efficiently leveraging human expertise.
  arXiv  Detail & Related papers  (2020-01-13T13:54:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.