VARS: Vision-based Assessment of Risk in Security Systems

• URL: http://arxiv.org/abs/2410.19642v1
• Date: Fri, 25 Oct 2024 15:47:13 GMT
• Title: VARS: Vision-based Assessment of Risk in Security Systems
• Authors: Pranav Gupta, Pratham Gohil, Sridhar S,
• Abstract summary: In this study, we perform a comparative analysis of various machine learning and deep learning models to predict danger ratings in a custom dataset of 100 videos. The danger ratings are classified into three categories: no alert (less than 7)and high alert (greater than equal to 7)
• Score: 1.433758865948252
• License:
• Abstract: The accurate prediction of danger levels in video content is critical for enhancing safety and security systems, particularly in environments where quick and reliable assessments are essential. In this study, we perform a comparative analysis of various machine learning and deep learning models to predict danger ratings in a custom dataset of 100 videos, each containing 50 frames, annotated with human-rated danger scores ranging from 0 to 10. The danger ratings are further classified into three categories: no alert (less than 7)and high alert (greater than equal to 7). Our evaluation covers classical machine learning models, such as Support Vector Machines, as well as Neural Networks, and transformer-based models. Model performance is assessed using standard metrics such as accuracy, F1-score, and mean absolute error (MAE), and the results are compared to identify the most robust approach. This research contributes to developing a more accurate and generalizable danger assessment framework for video-based risk detection.

Related papers

• A Hybrid Defense Strategy for Boosting Adversarial Robustness in Vision-Language Models [9.304845676825584]
  We propose a novel adversarial training framework that integrates multiple attack strategies and advanced machine learning techniques. Experiments conducted on real-world datasets, including CIFAR-10 and CIFAR-100, demonstrate that the proposed method significantly enhances model robustness.
  arXiv  Detail & Related papers  (2024-10-18T23:47:46Z)
• Unlearn and Burn: Adversarial Machine Unlearning Requests Destroy Model Accuracy [65.80757820884476]
  We expose a critical yet underexplored vulnerability in the deployment of unlearning systems. We present a threat model where an attacker can degrade model accuracy by submitting adversarial unlearning requests for data not present in the training set. We evaluate various verification mechanisms to detect the legitimacy of unlearning requests and reveal the challenges in verification.
  arXiv  Detail & Related papers  (2024-10-12T16:47:04Z)
• Evaluating Predictive Models in Cybersecurity: A Comparative Analysis of Machine and Deep Learning Techniques for Threat Detection [0.0]
  This paper examines and compares various machine learning as well as deep learning models to choose the most suitable ones for detecting and fighting against cybersecurity risks. The two datasets are used in the study to assess models like Naive Bayes, SVM, Random Forest, and deep learning architectures, i.e., VGG16, in the context of accuracy, precision, recall, and F1-score.
  arXiv  Detail & Related papers  (2024-07-08T15:05:59Z)
• Introducing v0.5 of the AI Safety Benchmark from MLCommons [101.98401637778638]
  This paper introduces v0.5 of the AI Safety Benchmark, which has been created by the MLCommons AI Safety Working Group. The benchmark has been designed to assess the safety risks of AI systems that use chat-tuned language models.
  arXiv  Detail & Related papers  (2024-04-18T15:01:00Z)
• 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)
• Analyzing and Mitigating Bias for Vulnerable Classes: Towards Balanced Representation in Dataset [2.143460356353513]
  This research focuses on investigating class imbalances among vulnerable road users. We utilize popular CNN models and Vision Transformers (ViTs) with the nuScenes dataset. Using the proposed mitigation approaches, we see improvement in IoU(%) and NDS(%) metrics from 71.3 to 75.6 and 80.6 to 83.7 for the CNN model.
  arXiv  Detail & Related papers  (2024-01-18T22:10:46Z)
• ASSERT: Automated Safety Scenario Red Teaming for Evaluating the Robustness of Large Language Models [65.79770974145983]
  ASSERT, Automated Safety Scenario Red Teaming, consists of three methods -- semantically aligned augmentation, target bootstrapping, and adversarial knowledge injection. We partition our prompts into four safety domains for a fine-grained analysis of how the domain affects model performance. We find statistically significant performance differences of up to 11% in absolute classification accuracy among semantically related scenarios and error rates of up to 19% absolute error in zero-shot adversarial settings.
  arXiv  Detail & Related papers  (2023-10-14T17:10:28Z)
• VSRQ: Quantitative Assessment Method for Safety Risk of Vehicle Intelligent Connected System [6.499974038759507]
  We develop a new model for vehicle risk assessment by combining I-FAHP with FCA clustering: VSRQ model. We evaluate the model on OpenPilot and experimentally demonstrate the effectiveness of the VSRQ model in identifying the safety of vehicle intelligent connected systems.
  arXiv  Detail & Related papers  (2023-05-03T05:08:56Z)
• Sample-Efficient Safety Assurances using Conformal Prediction [57.92013073974406]
  Early warning systems can provide alerts when an unsafe situation is imminent. To reliably improve safety, these warning systems should have a provable false negative rate. We present a framework that combines a statistical inference technique known as conformal prediction with a simulator of robot/environment dynamics.
  arXiv  Detail & Related papers  (2021-09-28T23:00:30Z)
• Anomaly Detection in Cybersecurity: Unsupervised, Graph-Based and Supervised Learning Methods in Adversarial Environments [63.942632088208505]
  Inherent to today's operating environment is the practice of adversarial machine learning. In this work, we examine the feasibility of unsupervised learning and graph-based methods for anomaly detection. We incorporate a realistic adversarial training mechanism when training our supervised models to enable strong classification performance in adversarial environments.
  arXiv  Detail & Related papers  (2021-05-14T10:05:10Z)

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.