A Hybrid Quantum-Classical AI-Based Detection Strategy for Generative Adversarial Network-Based Deepfake Attacks on an Autonomous Vehicle Traffic Sign Classification System

• URL: http://arxiv.org/abs/2409.17311v1
• Date: Wed, 25 Sep 2024 19:44:56 GMT
• Title: A Hybrid Quantum-Classical AI-Based Detection Strategy for Generative Adversarial Network-Based Deepfake Attacks on an Autonomous Vehicle Traffic Sign Classification System
• Authors: M Sabbir Salek, Shaozhi Li, Mashrur Chowdhury,
• Abstract summary: Authors present how a generative adversarial network-based deepfake attack can be crafted to fool the AV traffic sign classification systems. They develop a deepfake traffic sign image detection strategy leveraging hybrid quantum-classical neural networks (NNs) The results indicate that the hybrid quantum-classical NNs for deepfake detection could achieve similar or higher performance than the baseline classical convolutional NNs in most cases.
• Score: 2.962613983209398
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The perception module in autonomous vehicles (AVs) relies heavily on deep learning-based models to detect and identify various objects in their surrounding environment. An AV traffic sign classification system is integral to this module, which helps AVs recognize roadway traffic signs. However, adversarial attacks, in which an attacker modifies or alters the image captured for traffic sign recognition, could lead an AV to misrecognize the traffic signs and cause hazardous consequences. Deepfake presents itself as a promising technology to be used for such adversarial attacks, in which a deepfake traffic sign would replace a real-world traffic sign image before the image is fed to the AV traffic sign classification system. In this study, the authors present how a generative adversarial network-based deepfake attack can be crafted to fool the AV traffic sign classification systems. The authors developed a deepfake traffic sign image detection strategy leveraging hybrid quantum-classical neural networks (NNs). This hybrid approach utilizes amplitude encoding to represent the features of an input traffic sign image using quantum states, which substantially reduces the memory requirement compared to its classical counterparts. The authors evaluated this hybrid deepfake detection approach along with several baseline classical convolutional NNs on real-world and deepfake traffic sign images. The results indicate that the hybrid quantum-classical NNs for deepfake detection could achieve similar or higher performance than the baseline classical convolutional NNs in most cases while requiring less than one-third of the memory required by the shallowest classical convolutional NN considered in this study.

Related papers

• A Framework for the Systematic Assessment of Anomaly Detectors in Time-Sensitive Automotive Networks [0.4077787659104315]
  We present an assessment framework that allows for reproducible, comparable, and rapid evaluation of anomaly detection algorithms. We evaluate exemplary detection mechanisms and reveal how the detection performance is influenced by different combinations of TSN traffic flows and anomaly types.
  arXiv  Detail & Related papers  (2024-05-02T14:29:42Z)
• Exploring Geometry of Blind Spots in Vision Models [56.47644447201878]
  We study the phenomenon of under-sensitivity in vision models such as CNNs and Transformers. We propose a Level Set Traversal algorithm that iteratively explores regions of high confidence with respect to the input space. We estimate the extent of these connected higher-dimensional regions over which the model maintains a high degree of confidence.
  arXiv  Detail & Related papers  (2023-10-30T18:00:33Z)
• Explainable and Trustworthy Traffic Sign Detection for Safe Autonomous Driving: An Inductive Logic Programming Approach [0.0]
  We propose an ILP-based approach for stop sign detection in Autonomous Vehicles. It is more robust against adversarial attacks, as it mimics human-like perception. It is able to correctly identify all targeted stop signs, even in the presence of PR2 and ADvCam attacks.
  arXiv  Detail & Related papers  (2023-08-30T09:05:52Z)
• Reinforcement Learning based Cyberattack Model for Adaptive Traffic Signal Controller in Connected Transportation Systems [61.39400591328625]
  In a connected transportation system, adaptive traffic signal controllers (ATSC) utilize real-time vehicle trajectory data received from vehicles to regulate green time. This wirelessly connected ATSC increases cyber-attack surfaces and increases their vulnerability to various cyber-attack modes. One such mode is a'sybil' attack in which an attacker creates fake vehicles in the network. An RL agent is trained to learn an optimal rate of sybil vehicle injection to create congestion for an approach(s)
  arXiv  Detail & Related papers  (2022-10-31T20:12:17Z)
• Learning energy-efficient driving behaviors by imitating experts [75.12960180185105]
  This paper examines the role of imitation learning in bridging the gap between control strategies and realistic limitations in communication and sensing. We show that imitation learning can succeed in deriving policies that, if adopted by 5% of vehicles, may boost the energy-efficiency of networks with varying traffic conditions by 15% using only local observations.
  arXiv  Detail & Related papers  (2022-06-28T17:08:31Z)
• Efficient Federated Learning with Spike Neural Networks for Traffic Sign Recognition [70.306089187104]
  We introduce powerful Spike Neural Networks (SNNs) into traffic sign recognition for energy-efficient and fast model training. Numerical results indicate that the proposed federated SNN outperforms traditional federated convolutional neural networks in terms of accuracy, noise immunity, and energy efficiency as well.
  arXiv  Detail & Related papers  (2022-05-28T03:11:48Z)
• A Hybrid Defense Method against Adversarial Attacks on Traffic Sign Classifiers in Autonomous Vehicles [4.585587646404074]
  Adversarial attacks can make deep neural network (DNN) models predict incorrect output labels for autonomous vehicles (AVs) This study develops a resilient traffic sign classifier for AVs that uses a hybrid defense method. We find that our hybrid defense method achieves 99% average traffic sign classification accuracy for the no attack scenario and 88% average traffic sign classification accuracy for all attack scenarios.
  arXiv  Detail & Related papers  (2022-04-25T02:13:31Z)
• DeepHybrid: Deep Learning on Automotive Radar Spectra and Reflections for Object Classification [0.5669790037378094]
  We propose a method that combines classical radar signal processing and Deep Learning algorithms. The proposed method can be used for example to improve automatic emergency braking or collision avoidance systems.
  arXiv  Detail & Related papers  (2022-02-17T08:45:11Z)
• AVTPnet: Convolutional Autoencoder for AVTP anomaly detection in Automotive Ethernet Networks [2.415997479508991]
  In this paper, we propose a convolutional autoencoder (CAE) for offline detection of anomalies on the Audio Video Transport Protocol (AVTP) Our proposed approach is evaluated on the recently published " Automotive Ethernet Intrusion dataset"
  arXiv  Detail & Related papers  (2022-01-31T19:13:20Z)
• Robust Semi-supervised Federated Learning for Images Automatic Recognition in Internet of Drones [57.468730437381076]
  We present a Semi-supervised Federated Learning (SSFL) framework for privacy-preserving UAV image recognition. There are significant differences in the number, features, and distribution of local data collected by UAVs using different camera modules. We propose an aggregation rule based on the frequency of the client's participation in training, namely the FedFreq aggregation rule.
  arXiv  Detail & Related papers  (2022-01-03T16:49:33Z)
• Deep traffic light detection by overlaying synthetic context on arbitrary natural images [49.592798832978296]
  We propose a method to generate artificial traffic-related training data for deep traffic light detectors. This data is generated using basic non-realistic computer graphics to blend fake traffic scenes on top of arbitrary image backgrounds. It also tackles the intrinsic data imbalance problem in traffic light datasets, caused mainly by the low amount of samples of the yellow state.
  arXiv  Detail & Related papers  (2020-11-07T19:57:22Z)

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.