MacroIC/Multi-domain-DataSet

Multi-domain DataSet

This repository provides data from two vehicle models, Vehicle_A and Vehicle_B, across three domains: CAN, perception, and planning. The data support comparisons of system inputs and outputs under normal and attack conditions.

Data Files

Domain	Vehicle_A	Vehicle_B
CAN	Dataset_CAN_Vehicle_A.zip	Dataset_CAN_Vehicle_B.zip
Perception	Perception_Vehicle_A.zip	Perception_Vehicle_B.zip
Planning	Planning_Vehicle_A.zip	Planning_Vehicle_B.zip

Code Availability

Custom attack-generation and batch-processing code was used to construct the dataset.

File	Purpose	File Type
attack_api_det.py	Implements perception-domain adversarial image generation, including FGSM and PGD	Python source code
run_det_attacks.py	Generates adversarial images in batches and invokes the detector to produce results	Python source code
best.pt	Model weights used for attack generation	Model checkpoint
main	Perception detector provided by the company	Closed-source executable

The underlying source code of main is not publicly available because of the company's intellectual property restrictions. Code for desensitisation and for constructing the CAN and planning datasets is not included in these four files.

Usage Notes

Downloading and Reading the Files

Download the required ZIP archives and extract each into a separate directory to avoid mixing vehicle directories with identical names across domains. For example:

from huggingface_hub import hf_hub_download
from zipfile import ZipFile

archive = hf_hub_download(
    repo_id="MacroIC/Multi-domain-DataSet",
    repo_type="dataset",
    filename="Perception_Vehicle_A.zip",
)
with ZipFile(archive) as z:
    z.extractall("data/perception")

Domain	How to Read the Files	Location of Normal and Attack Data
CAN	ASC files are text-based message logs. Read timestamps, CAN IDs, payloads, and any available labels line by line. File headers and end markers are not messages.	Normal contains normal recordings. Attack is organised into DoS, Fuzzy, Replay, and Spoofing conditions. An attack directory identifies the recording's experimental condition; it does not mean that every message in the file is an injected attack message.
Perception	Load input images from images using an image library and parse the whitespace-separated detection results in result/txt. Files in result/images are visualisations.	ori denotes Clean. FGSM and PGD denote the corresponding attack conditions. Remove the _adv_FGSM or _adv_PGD suffix from an image's filename stem to match its Clean counterpart within the same run.
Planning	.log files contain text records. Read planning requests and available results by their field names.	requests/clean contains normal requests. requests/attack is organised by attack type, and attack logs contain an ATTACK_TYPE marker. Corresponding results are stored under results.

Multiple ASC files may be consecutive segments of a single CAN recording. File counts should therefore not be treated as counts of independent recordings. To identify attack messages or exact attack start and end times, use acquisition records and per-message labels rather than directory names alone.

Perception TXT Format and Class Identifiers

The image resolution is 448×416 pixels (width×height). Each non-empty TXT line contains one predicted box:

x1 y1 x2 y2 x3 y3 x4 y4 class_id confidence

The first eight fields specify the coordinates of four vertices at the original image scale. The final two fields are the class identifier and confidence score. Each TXT file shares its filename stem with the corresponding input image. An empty TXT file indicates that no detection boxes were output for that frame.

Generating Perception Attacks

Generate FGSM images:

python run_det_attacks.py \
  --weights ./best.pt \
  --source data/Vehicle_A/Double_Occlusion_16/ori/images \
  --out generated/Vehicle_A/Double_Occlusion_16 \
  --methods FGSM --eps 0.031372549 --batch 4 --img-size 640 \
  --device 0 --n-samples 999 \
  --method-config '{"FGSM":{"eps":0.031372549}}' \
  --main_exec ./main

Generate PGD images:

python run_det_attacks.py \
  --weights ./best.pt \
  --source data/Vehicle_A/Double_Occlusion_16/ori/images \
  --out generated/Vehicle_A/Double_Occlusion_16 \
  --methods PGD --eps 0.031372549 --batch 4 --img-size 640 \
  --device 0 --n-samples 999 \
  --method-config '{"PGD":{"eps":0.031372549,"alpha":0.01,"iters":40}}' \
  --main_exec ./main

For other runs, replace the vehicle and run directories in both --source and --out. The --n-samples option processes only the first N existing images sorted by filename; it does not create an ori directory. If fewer than N images are available, all available images are processed. The current runner does not pass --eps directly to the attack interface, so these examples explicitly set the effective epsilon through --method-config. PGD uses the step size and iteration count shown above, with the interface's default random initial perturbation.

Generated input images are saved to <out>/FGSM/images or <out>/PGD/images. The runner then executes ./main images/ in the method directory, where the company's detector generates result/images and result/txt. These commands do not automatically apply the coordinate clipping used for the released TXT files and do not guarantee byte-for-byte reproduction of the existing adversarial images.

Dataset Construction Files

File	Role
run_det_attacks.py	Main entry point: parses arguments, loads weights, reads input images, invokes the attacker, saves adversarial images, and runs the detector
function/attack/attack_api_det.py	Implements FGSM/PGD for detection tasks, letterbox preprocessing, the proxy objective, gradient updates, perturbation constraints, and image saving
best.pt	YOLOv7 checkpoint containing a model instance and weights; it still requires matching YOLOv7 source code, PyTorch, and related dependencies
main	Compiled detector provided by the company; generates result images and TXT files. Its underlying source code is not distributed with the data.
<yolov7>/data/fisheye8k.yaml	Class configuration file from the original training project. It is not included among the four files intended for upload, and its class names cannot be used directly to interpret the company's detector TXT identifiers.

License

The dataset retains the repository's existing CC BY 4.0 declaration. This declaration does not automatically grant permission to publicly distribute unpublished company executables, model weights, or third-party code.