Generation of datasets for Adversarial Robustness evaluation
Welcome to the CARLA-GeAR main page
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
- Semantic segmentation (DDRNet, BiSeNet) - LINK
- 2d Object Detection (Faster R-CNN, RetinaNet) - LINK
- Monocular Depth estimation (GLPDepth, AdaBins) - LINK
- Stereo 3d object detection (Stereo R-CNN) - LINK
- a train and val split that do not contain any patches and can be used to craft new ones.
- A test_net split that includes white-box patches targeting a specific model.
- A test_random and test_nopatch split that are “clones” of the test_nest split (i.e., same seeds), but includes a random patch and no patch respectively. These splits may be used for fair performance comparisons.
F. Nesti, G. Rossolini, S. Nair, A. Biondi, G. Buttazzo. “Evaluating the robustness of semantic segmentation for autonomous driving against real-world adversarial patch attacks” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (2022)
G. Rossolini, F. Nesti, G. D’Amico, S. Nair, A. Biondi, G. Buttazzo. “On the Real-World Adversarial Robustness of Real-Time Semantic Segmentation Models for Autonomous Driving”. Submitted. Preprint:
G. Rossolini, F. Nesti, F. Brau, A. Biondi, G. Buttazzo. “Defending From Physically-Realizable Adversarial Attacks Through Internal Over-Activation Analysis”. Submitted. Preprint: