neural network robustness

Backdooring Rationalization AAAI 2026 HiQ-Lip: A Hierarchical Quantum-Classical Method for Global Lipschitz Constant Estimation of ReLU Networks AAAI 2026 Adversarial Attention Perturbations for Large Object Detection Transformers ICCV 2025 Contrasting Adversarial Perturbations: The Space of Harmless Perturbations AAAI 2025 Convex Formulations for Training Two-Layer ReLU Neural Networks ICLR 2025 Improving Generalization of Universal Adversarial Perturbation via Dynamic Maximin Optimization AAAI 2025 MOS-Attack: A Scalable Multi-objective Adversarial Attack Framework CVPR 2025 Set-Valued Sensitivity Analysis of Deep Neural Networks AAAI 2025 When Visual State Space Model Meets Backdoor Attacks WACV 2025 1-Lipschitz Layers Compared: Memory Speed and Certifiable Robustness CVPR 2024 Achieving Domain-Independent Certified Robustness via Knowledge Continuity NeurIPS 2024 Assessing Neural Network Robustness via Adversarial Pivotal Tuning WACV 2024 Convex neural network synthesis for robustness in the 1-norm L4DC 2024 ECLipsE: Efficient Compositional Lipschitz Constant Estimation for Deep Neural Networks NeurIPS 2024 ImageNet-D: Benchmarking Neural Network Robustness on Diffusion Synthetic Object CVPR 2024 Improving Robustness via Tilted Exponential Layer: A Communication-Theoretic Perspective AISTATS 2024 Neuron Activation Coverage: Rethinking Out-of-distribution Detection and Generalization ICLR 2024 Single Domain Generalization via Normalised Cross-Correlation Based Convolutions WACV 2024 SuperDeepFool: a new fast and accurate minimal adversarial attack NeurIPS 2024 Taxonomy Driven Fast Adversarial Training AAAI 2024 Towards General Robustness Verification of MaxPool-based Convolutional Neural Networks via Tightening Linear Approximation CVPR 2024 Adversarial Parameter Attack on Deep Neural Networks ICML 2023 Characterizing the Impacts of Instances on Robustness ACL 2023 COLA: Orchestrating Error Coding and Learning for Robust Neural Network Inference Against Hardware Defects ICML 2023 DENL: Diverse Ensemble and Noisy Logits for Improved Robustness of Neural Networks ACML 2023