#This is the official implementation and case study of our paper: Toward Robust and Accurate Adversarial Camouflage Generation against Vehicle Detectors
The paper can be find in here. Source code can be find in here
Adversarial camouflage is a widely used physical attack against vehicle detectors for its superiority in multi-view attack performance. One promising approach involves using differentiable neural renderers to facilitate adversarial camouflage optimization through gradient back-propagation. However, existing methods often struggle to capture environmental characteristics during the rendering process or produce adversarial textures that can precisely map to the target vehicle. Moreover, these approaches neglect diverse weather conditions, reducing the efficacy of generated camouflage across varying weather scenarios. To tackle these challenges, we propose a robust and accurate camouflage generation method, namely RAUCA. The core of RAUCA is a novel neural rendering component, End-to-End Neural Renderer Plus (E2E-NRP), which can accurately optimize and project vehicle textures and render images with environmental characteristics such as lighting and weather. In addition, we integrate a multi-weather dataset for camouflage generation, leveraging the E2E-NRP to enhance the attack robustness. Experimental results on six popular object detectors show that RAUCA-final outperforms existing methods in both simulation and real-world settings.
The overview of RAUCA. First, we create a multi-weather dataset using CARLA, which includes car images, corresponding mask images, and camera transformation sets. Then, the car images are segmented using the mask images to obtain the foreground car and background images. The foreground car image, the 3D model, and the camera transformation are passed through the E2E-NRP rendering component for rendering. The rendered image is then seamlessly integrated with the background. After a series of random output augmentation, the image is fed into the object detector. Finally, we optimize the adversarial camouflage through back-propagation with our devised loss function computed from the output of the object detector.
before you running the code, you must install the neural renderer python package. You need to pull Our implementation here, which is capable of end-to-end optimization of the UV map with improved sampling technique from the UV map to a facet-based tensor.
other requirements are listed in src/requirements.txt
Note that, our code is based on Yolo-V3 implementation. Dowdload the YOLO-V3 weight from here and put it into src folder.
The generalized object EFE weight and finetuned EFE weight for car can be find.
The latest dataset for adversarial camouflage generation can get Baidu Cload disk or IEEE Dataset
We offer the pre-trained EFE weight and Audi Etron's EFE weight
TO train NRP:
python src/NRP.pyTO get camouflage:
python src/generate_camouflage_E2E.pyThe generated texture of RAUCA-E2E is in the form of .png, which can be deployed directly, we offer our optimized result here.
