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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 401–450 of 1746 papers

TitleStatusHype
Dropping Pixels for Adversarial Robustness—0
Dual Manifold Adversarial Robustness: Defense against Lp and non-Lp Adversarial Attacks—0
Calibration and Consistency of Adversarial Surrogate Losses—0
Adversarially Robust Neural Networks via Optimal Control: Bridging Robustness with Lyapunov Stability—0
Double Visual Defense: Adversarial Pre-training and Instruction Tuning for Improving Vision-Language Model Robustness—0
Don't let your Discriminator be fooled—0
Adversarial Robustness via Label-Smoothing—0
CAMUS: A Framework to Build Formal Specifications for Deep Perception Systems Using Simulators—0
Can Attention Masks Improve Adversarial Robustness?—0
Can collaborative learning be private, robust and scalable?—0
Can Implicit Bias Imply Adversarial Robustness?—0
Can Language Models be Instructed to Protect Personal Information?—0
Don't Retrain, Just Rewrite: Countering Adversarial Perturbations by Rewriting Text—0
Canonical Latent Representations in Conditional Diffusion Models—0
CAP-GAN: Towards Adversarial Robustness with Cycle-consistent Attentional Purification—0
Buffer Zone based Defense against Adversarial Examples in Image Classification—0
CARE: Ensemble Adversarial Robustness Evaluation Against Adaptive Attackers for Security Applications—0
Adversarial Robustness: What fools you makes you stronger—0
Adversarial Robustness via Adaptive Label Smoothing—0
Adversarially Robust Spiking Neural Networks with Sparse Connectivity—0
Adversarial Robustness without Adversarial Training: A Teacher-Guided Curriculum Learning Approach—0
Catastrophic Overfitting: A Potential Blessing in Disguise—0
Adversarial Robustness with Semi-Infinite Constrained Learning—0
Achieving Adversarial Robustness Requires An Active Teacher—0
Adversarial Test on Learnable Image Encryption—0
Causal Adversarial Perturbations for Individual Fairness and Robustness in Heterogeneous Data Spaces—0
Causal Fair Metric: Bridging Causality, Individual Fairness, and Adversarial Robustness—0
Causal Feature Selection for Responsible Machine Learning—0
Causal Information Bottleneck Boosts Adversarial Robustness of Deep Neural Network—0
CausalPC: Improving the Robustness of Point Cloud Classification by Causal Effect Identification—0
Adversarially Robust Neural Architectures—0
Do Wider Neural Networks Really Help Adversarial Robustness?—0
Do Gradient-based Explanations Tell Anything About Adversarial Robustness to Android Malware?—0
Dynamical Low-Rank Compression of Neural Networks with Robustness under Adversarial Attacks—0
Distance-Restricted Explanations: Theoretical Underpinnings & Efficient Implementation—0
Certified Adversarial Robustness for Deep Reinforcement Learning—0
Adversarial Robustness Unhardening via Backdoor Attacks in Federated Learning—0
Distilled Agent DQN for Provable Adversarial Robustness—0
Adversarially Robust Industrial Anomaly Detection Through Diffusion Model—0
Distilling Adversarial Robustness Using Heterogeneous Teachers—0
Bridged Adversarial Training—0
AdPO: Enhancing the Adversarial Robustness of Large Vision-Language Models with Preference Optimization—0
Training Graph Neural Networks Using Non-Robust Samples—0
Boosting Certified Robustness for Time Series Classification with Efficient Self-Ensemble—0
Adversarially Robust Few-shot Learning via Parameter Co-distillation of Similarity and Class Concept Learners—0
Certified Robustness to Word Substitution Attack with Differential Privacy—0
A Survey and Evaluation of Adversarial Attacks for Object Detection—0
Boosting Barely Robust Learners: A New Perspective on Adversarial Robustness—0
AdvFilter: Predictive Perturbation-aware Filtering against Adversarial Attack via Multi-domain Learning—0
Disentangled Text Representation Learning with Information-Theoretic Perspective for 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