
Replication Package for the Paper "A Taxonomy of System-Level Attacks on Deep Learning Models in Autonomous Vehicles" This repository contains the replication package associated with the paper titled "A Taxonomy of System-Level Attacks on Deep Learning Models in Autonomous Vehicles." Contents ChatGPT_for_AVs.pdf: This document presents the results of a structured ChatGPT search aimed at identifying the relevant domains of autonomous vehicles, deep learning models and modules, and simulation environments. How to Replicate Install findpapers using the following command: pip install findpapers Open the send_query.py file to find the query used for paper extraction. Copy and execute the query to retrieve relevant research papers. Update the json_dir_path = './findpapers' variable in venue_filter.py to match the directory containing the list of downloaded papers. Execute venue_filter.py to filter papers based on the venues specified in the chosen_venues.txt file. The output will be a file named papers_after_venue_filter.csv, which includes the title, abstract, URL, venue, and publication date for each filtered paper. Note: The venue_freq.py script calculates the frequency of papers per venue, distinguishing between journal and conference venues. Running this script is optional and was primarily used during our venue selection process. Citation Tehrani, M. J., Kim, J., Foulefack, R. Z. L., Marchetto, A., & Tonella, P. (2024).A Taxonomy of System-Level Attacks on Deep Learning Models in Autonomous Vehicles.https://arxiv.org/abs/2412.04510 @article{tehrani2024taxonomy, title={A Taxonomy of System-Level Attacks on Deep Learning Models in Autonomous Vehicles}, author={Tehrani, Masoud Jamshidiyan and Kim, Jinhan and Foulefack, Rosmael Zidane Lekeufack and Marchetto, Alessandro and Tonella, Paolo}, journal={arXiv preprint arXiv:2412.04510}, year={2024} }
Autonomous Vehicle Security, Deep Learning Security, Security Testing
Autonomous Vehicle Security, Deep Learning Security, Security Testing
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
