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Adversarial Robustness

Adversarial Robustness evaluates the vulnerabilities of machine learning models under various types of adversarial attacks.

Papers

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Showing 1151–1200 of 1746 papers

TitleStatusHype
Holistic Adversarial Robustness of Deep Learning Models—0
Unlabeled Data Help: Minimax Analysis and Adversarial Robustness—0
Finding Dynamics Preserving Adversarial Winning Tickets—0
Boosting Barely Robust Learners: A New Perspective on Adversarial Robustness—0
D4: Detection of Adversarial Diffusion Deepfakes Using Disjoint Ensembles—0
Deadwooding: Robust Global Pruning for Deep Neural Networks—0
On The Empirical Effectiveness of Unrealistic Adversarial Hardening Against Realistic Adversarial AttacksCode0
Optimized Potential Initialization for Low-latency Spiking Neural Networks—0
Finding Biological Plausibility for Adversarially Robust Features via Metameric TasksCode0
Smoothed Embeddings for Certified Few-Shot LearningCode0
Adversarial Robustness in Deep Learning: Attacks on Fragile Neurons—0
Improving Robustness by Enhancing Weak SubnetsCode0
The Many Faces of Adversarial Risk—0
Unveiling Project-Specific Bias in Neural Code Models—0
Tools and Practices for Responsible AI Engineering—0
Towards Adversarially Robust Deep Image Denoising—0
GenLabel: Mixup Relabeling using Generative Models—0
Rethinking Feature Uncertainty in Stochastic Neural Networks for Adversarial Robustness—0
Improving the Behaviour of Vision Transformers with Token-consistent Stochastic Layers—0
Associative Adversarial Learning Based on Selective Attack—0
Perlin Noise Improve Adversarial Robustness—0
Understanding and Measuring Robustness of Multimodal Learning—0
Improving Robustness with Image Filtering—0
On the Adversarial Robustness of Causal Algorithmic RecourseCode0
Certified Federated Adversarial Training—0
The King is Naked: on the Notion of Robustness for Natural Language ProcessingCode0
Analyzing and Improving the Robustness of Tabular Classifiers using Counterfactual ExplanationsCode0
Spatial-Temporal-Fusion BNN: Variational Bayesian Feature Layer—0
On Adversarial Robustness of Point Cloud Semantic SegmentationCode0
Robustness Certificates for Implicit Neural Networks: A Mixed Monotone Contractive Approach—0
SNEAK: Synonymous Sentences-Aware Adversarial Attack on Natural Language Video Localization—0
On the Existence of the Adversarial Bayes Classifier (Extended Version)—0
Is Approximation Universally Defensive Against Adversarial Attacks in Deep Neural Networks?—0
Adversarial Robustness of Deep Reinforcement Learning based Dynamic Recommender Systems—0
Formalizing Generalization and Adversarial Robustness of Neural Networks to Weight Perturbations—0
Towards Stable and Robust AdderNets—0
Push Stricter to Decide Better: A Class-Conditional Feature Adaptive Framework for Improving Adversarial RobustnessCode0
Certified Adversarial Defenses Meet Out-of-Distribution Corruptions: Benchmarking Robustness and Simple Baselines—0
Adversarially Robust 3D Point Cloud Recognition Using Self-Supervisions—0
A Systematic Review of Robustness in Deep Learning for Computer Vision: Mind the gap?—0
Clustering Effect of Adversarial Robust Models—0
On the Existence of The Adversarial Bayes Classifier—0
Exponential Separation between Two Learning Models and Adversarial Robustness—0
Adversarial Robustness without Adversarial Training: A Teacher-Guided Curriculum Learning Approach—0
Robustness between the worst and average caseCode0
Clustering Effect of (Linearized) Adversarial Robust ModelsCode0
Adversarial Examples on Segmentation Models Can be Easy to Transfer—0
Revisiting Adversarial Robustness of Classifiers With a Reject Option—0
Heterogeneous Architecture Search Approach within Adversarial Dynamic Defense Framework—0
The Diversity Metrics of Sub-models based on SVD of Jacobians for Ensembles Adversarial Robustness—0
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All datasetsAdvGLUECIFAR-10ImageNetImageNet-AImageNet-CStylized ImageNetCIFAR-100

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DeBERTa (single model)Accuracy0.61—Unverified
2ALBERT (single model)Accuracy0.59—Unverified
3T5 (single model)Accuracy0.57—Unverified
4SMART_RoBERTa (single model)Accuracy0.54—Unverified
5FreeLB (single model)Accuracy0.5—Unverified
6RoBERTa (single model)Accuracy0.5—Unverified
7InfoBERT (single model)Accuracy0.46—Unverified
8ELECTRA (single model)Accuracy0.42—Unverified
9BERT (single model)Accuracy0.34—Unverified
10SMART_BERT (single model)Accuracy0.3—Unverified
#ModelMetricClaimedVerifiedStatus
1Mixed classifierAccuracy95.23—Unverified
2Stochastic-LWTA/PGD/WideResNet-34-10Accuracy92.26—Unverified
3Stochastic-LWTA/PGD/WideResNet-34-5Accuracy91.88—Unverified
4GLOT-DRAccuracy84.13—Unverified
5TRADES-ANCRA/ResNet18Accuracy81.7—Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet-50 (SGD, Cosine)Accuracy77.4—Unverified
2ResNet-50 (SGD, Step)Accuracy76.9—Unverified
3DeiT-S (AdamW, Cosine)Accuracy76.8—Unverified
4ResNet-50 (AdamW, Cosine)Accuracy76.4—Unverified
#ModelMetricClaimedVerifiedStatus
1DeiT-S (AdamW, Cosine)Accuracy12.2—Unverified
2ResNet-50 (SGD, Cosine)Accuracy3.3—Unverified
3ResNet-50 (SGD, Step)Accuracy3.2—Unverified
4ResNet-50 (AdamW, Cosine)Accuracy3.1—Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet-50 (AdamW, Cosine)mean Corruption Error (mCE)59.3—Unverified
2ResNet-50 (SGD, Step)mean Corruption Error (mCE)57.9—Unverified
3ResNet-50 (SGD, Cosine)mean Corruption Error (mCE)56.9—Unverified
4DeiT-S (AdamW, Cosine)mean Corruption Error (mCE)48—Unverified
#ModelMetricClaimedVerifiedStatus
1DeiT-S (AdamW, Cosine)Accuracy13—Unverified
2ResNet-50 (SGD, Cosine)Accuracy8.4—Unverified
3ResNet-50 (SGD, Step)Accuracy8.3—Unverified
4ResNet-50 (AdamW, Cosine)Accuracy8.1—Unverified
#ModelMetricClaimedVerifiedStatus
1Mixed ClassifierClean Accuracy85.21—Unverified
2ResNet18/MART-ANCRAClean Accuracy60.1—Unverified