Benchmarking the Spatial Robustness of DNNs via Natural and Adversarial Localized Corruptions
Fuente:
arXiv
Saved in:
| Main Authors: | Pietrosanti, Giulia Marchiori, Rossolini, Giulio, Biondi, Alessandro, Buttazzo, Giorgio |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Attention-Based Real-Time Defenses for Physical Adversarial Attacks in Vision Applications
by: Rossolini, Giulio, et al.
Published: (2023)
by: Rossolini, Giulio, et al.
Published: (2023)
Edge-Only Universal Adversarial Attacks in Distributed Learning
by: Rossolini, Giulio, et al.
Published: (2024)
by: Rossolini, Giulio, et al.
Published: (2024)
Defending From Physically-Realizable Adversarial Attacks Through Internal Over-Activation Analysis
by: Rossolini, Giulio, et al.
Published: (2022)
by: Rossolini, Giulio, et al.
Published: (2022)
On the Real-World Adversarial Robustness of Real-Time Semantic Segmentation Models for Autonomous Driving
by: Rossolini, Giulio, et al.
Published: (2022)
by: Rossolini, Giulio, et al.
Published: (2022)
CARLA-GeAR: a Dataset Generator for a Systematic Evaluation of Adversarial Robustness of Vision Models
by: Nesti, Federico, et al.
Published: (2022)
by: Nesti, Federico, et al.
Published: (2022)
Learning Robustness at Test-Time from a Non-Robust Teacher
by: Bianchettin, Stefano, et al.
Published: (2026)
by: Bianchettin, Stefano, et al.
Published: (2026)
PairedGTA: Generating Driving Datasets for Controlled Photometric Shift Analysis
by: Chianese, Andrea, et al.
Published: (2026)
by: Chianese, Andrea, et al.
Published: (2026)
Increasing the Confidence of Deep Neural Networks by Coverage Analysis
by: Rossolini, Giulio, et al.
Published: (2021)
by: Rossolini, Giulio, et al.
Published: (2021)
Exploiting Edge Features for Transferable Adversarial Attacks in Distributed Machine Learning
by: Rossolini, Giulio, et al.
Published: (2025)
by: Rossolini, Giulio, et al.
Published: (2025)
Video Deblurring by Sharpness Prior Detection and Edge Information
by: Tian, Yang, et al.
Published: (2025)
by: Tian, Yang, et al.
Published: (2025)
Towards Compact and Robust DNNs via Compression-aware Sharpness Minimization
by: He, Jialuo, et al.
Published: (2026)
by: He, Jialuo, et al.
Published: (2026)
Evaluating the Impact of Compression Techniques on the Robustness of CNNs under Natural Corruptions
by: Da Silva, Itallo Patrick Castro Alves, et al.
Published: (2025)
by: Da Silva, Itallo Patrick Castro Alves, et al.
Published: (2025)
PoseBench: Benchmarking the Robustness of Pose Estimation Models under Corruptions
by: Ma, Sihan, et al.
Published: (2024)
by: Ma, Sihan, et al.
Published: (2024)
Investigating Calibration and Corruption Robustness of Post-hoc Pruned Perception CNNs: An Image Classification Benchmark Study
by: Mitra, Pallavi, et al.
Published: (2024)
by: Mitra, Pallavi, et al.
Published: (2024)
Benchmarking Content-Based Puzzle Solvers on Corrupted Jigsaw Puzzles
by: Dirauf, Richard, et al.
Published: (2025)
by: Dirauf, Richard, et al.
Published: (2025)
VLM-RobustBench: A Comprehensive Benchmark for Robustness of Vision-Language Models
by: Saxena, Rohit, et al.
Published: (2026)
by: Saxena, Rohit, et al.
Published: (2026)
MultiCorrupt: A Multi-Modal Robustness Dataset and Benchmark of LiDAR-Camera Fusion for 3D Object Detection
by: Beemelmanns, Till, et al.
Published: (2024)
by: Beemelmanns, Till, et al.
Published: (2024)
Predicting and Enhancing the Fairness of DNNs with the Curvature of Perceptual Manifolds
by: Ma, Yanbiao, et al.
Published: (2023)
by: Ma, Yanbiao, et al.
Published: (2023)
Investigating the Corruption Robustness of Image Classifiers with Random Lp-norm Corruptions
by: Siedel, Georg, et al.
Published: (2023)
by: Siedel, Georg, et al.
Published: (2023)
Promoting Shape Bias in CNNs: Frequency-Based and Contrastive Regularization for Corruption Robustness
by: Ranabhat, Robin Narsingh, et al.
Published: (2025)
by: Ranabhat, Robin Narsingh, et al.
Published: (2025)
Dynamic Sparse Training versus Dense Training: The Unexpected Winner in Image Corruption Robustness
by: Wu, Boqian, et al.
Published: (2024)
by: Wu, Boqian, et al.
Published: (2024)
MSC-Bench: Benchmarking and Analyzing Multi-Sensor Corruption for Driving Perception
by: Hao, Xiaoshuai, et al.
Published: (2025)
by: Hao, Xiaoshuai, et al.
Published: (2025)
Unveiling and Mitigating Generalized Biases of DNNs through the Intrinsic Dimensions of Perceptual Manifolds
by: Ma, Yanbiao, et al.
Published: (2024)
by: Ma, Yanbiao, et al.
Published: (2024)
MindSet: Vision. A toolbox for testing DNNs on key psychological experiments
by: Biscione, Valerio, et al.
Published: (2024)
by: Biscione, Valerio, et al.
Published: (2024)
Robust SAM: On the Adversarial Robustness of Vision Foundation Models
by: Long, Jiahuan, et al.
Published: (2025)
by: Long, Jiahuan, et al.
Published: (2025)
ALA: Naturalness-aware Adversarial Lightness Attack
by: Huang, Yihao, et al.
Published: (2022)
by: Huang, Yihao, et al.
Published: (2022)
Boosting the Transferability of Adversarial Examples via Local Mixup and Adaptive Step Size
by: Liu, Junlin, et al.
Published: (2024)
by: Liu, Junlin, et al.
Published: (2024)
Robust Asymmetric Heterogeneous Federated Learning with Corrupted Clients
by: Fang, Xiuwen, et al.
Published: (2025)
by: Fang, Xiuwen, et al.
Published: (2025)
How Worst-Case Are Adversarial Attacks? Linking Adversarial and Perturbation Robustness
by: Rossolini, Giulio
Published: (2026)
by: Rossolini, Giulio
Published: (2026)
On the Adversarial Robustness of Discrete Image Tokenizers
by: Bhagwatkar, Rishika, et al.
Published: (2026)
by: Bhagwatkar, Rishika, et al.
Published: (2026)
OODFace: Benchmarking Robustness of Face Recognition under Common Corruptions and Appearance Variations
by: Kang, Caixin, et al.
Published: (2024)
by: Kang, Caixin, et al.
Published: (2024)
Pre-Deployment Robustness Stress Testing for CT Segmentation Systems Using Clinically Motivated Multi-Corruption Augmentation
by: Kang, CholMin, et al.
Published: (2026)
by: Kang, CholMin, et al.
Published: (2026)
Robust Adverse Weather Removal via Spectral-based Spatial Grouping
by: Jeong, Yuhwan, et al.
Published: (2025)
by: Jeong, Yuhwan, et al.
Published: (2025)
CLIPure: Purification in Latent Space via CLIP for Adversarially Robust Zero-Shot Classification
by: Zhang, Mingkun, et al.
Published: (2025)
by: Zhang, Mingkun, et al.
Published: (2025)
CIARD: Cyclic Iterative Adversarial Robustness Distillation
by: Lu, Liming, et al.
Published: (2025)
by: Lu, Liming, et al.
Published: (2025)
On Inherent Adversarial Robustness of Active Vision Systems
by: Mukherjee, Amitangshu, et al.
Published: (2024)
by: Mukherjee, Amitangshu, et al.
Published: (2024)
Revisiting the Robust Generalization of Adversarial Prompt Tuning
by: Yang, Fan, et al.
Published: (2024)
by: Yang, Fan, et al.
Published: (2024)
Detecting and Corrupting Convolution-based Unlearnable Examples
by: Li, Minghui, et al.
Published: (2023)
by: Li, Minghui, et al.
Published: (2023)
Defining and Extracting generalizable interaction primitives from DNNs
by: Chen, Lu, et al.
Published: (2024)
by: Chen, Lu, et al.
Published: (2024)
Bridging the Gap: A Framework for Real-World Video Deepfake Detection via Social Network Compression Emulation
by: Montibeller, Andrea, et al.
Published: (2025)
by: Montibeller, Andrea, et al.
Published: (2025)
Similar Items
-
Attention-Based Real-Time Defenses for Physical Adversarial Attacks in Vision Applications
by: Rossolini, Giulio, et al.
Published: (2023) -
Edge-Only Universal Adversarial Attacks in Distributed Learning
by: Rossolini, Giulio, et al.
Published: (2024) -
Defending From Physically-Realizable Adversarial Attacks Through Internal Over-Activation Analysis
by: Rossolini, Giulio, et al.
Published: (2022) -
On the Real-World Adversarial Robustness of Real-Time Semantic Segmentation Models for Autonomous Driving
by: Rossolini, Giulio, et al.
Published: (2022) -
CARLA-GeAR: a Dataset Generator for a Systematic Evaluation of Adversarial Robustness of Vision Models
by: Nesti, Federico, et al.
Published: (2022)