SOTAVerified

Adversarial Robustness

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

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1501–1550 of 1746 papers

TitleStatusHype
A Comprehensive Study on Robustness of Image Classification Models: Benchmarking and Rethinking—0
Developing Assurance Cases for Adversarial Robustness and Regulatory Compliance in LLMs—0
Canonical Latent Representations in Conditional Diffusion Models—0
Differentially Private Adversarial Robustness Through Randomized Perturbations—0
Differentially Private Optimizers Can Learn Adversarially Robust Models—0
A3T: Adversarially Augmented Adversarial Training—0
Adversarial Robustness of Link Sign Prediction in Signed Graphs—0
DiffuseMix: Label-Preserving Data Augmentation with Diffusion Models—0
Scoring Black-Box Models for Adversarial Robustness—0
Two Heads are Better than One: Towards Better Adversarial Robustness by Combining Transduction and Rejection—0
Certified Robustness to Clean-Label Poisoning Using Diffusion Denoising—0
DiPSeN: Differentially Private Self-normalizing Neural Networks For Adversarial Robustness in Federated Learning—0
Discretization based Solutions for Secure Machine Learning against Adversarial Attacks—0
Disentangled Text Representation Learning with Information-Theoretic Perspective for Adversarial Robustness—0
Second Order Optimization for Adversarial Robustness and Interpretability—0
Two is Better than One: Efficient Ensemble Defense for Robust and Compact Models—0
Dissecting Local Properties of Adversarial Examples—0
Can Language Models be Instructed to Protect Personal Information?—0
Distance-Restricted Explanations: Theoretical Underpinnings & Efficient Implementation—0
Distilled Agent DQN for Provable Adversarial Robustness—0
Distilling Adversarial Robustness Using Heterogeneous Teachers—0
Can Implicit Bias Imply Adversarial Robustness?—0
Does Adversarial Robustness Really Imply Backdoor Vulnerability?—0
SecPE: Secure Prompt Ensembling for Private and Robust Large Language Models—0
Secure Diagnostics: Adversarial Robustness Meets Clinical Interpretability—0
Do Wider Neural Networks Really Help Adversarial Robustness?—0
Do Gradient-based Explanations Tell Anything About Adversarial Robustness to Android Malware?—0
Adversarially Robust Neural Networks via Optimal Control: Bridging Robustness with Lyapunov Stability—0
Don't let your Discriminator be fooled—0
SAM Meets UAP: Attacking Segment Anything Model With Universal Adversarial Perturbation—0
Don't Retrain, Just Rewrite: Countering Adversarial Perturbations by Rewriting Text—0
2-in-1 Accelerator: Enabling Random Precision Switch for Winning Both Adversarial Robustness and Efficiency—0
Double Visual Defense: Adversarial Pre-training and Instruction Tuning for Improving Vision-Language Model Robustness—0
Can collaborative learning be private, robust and scalable?—0
Can Attention Masks Improve Adversarial Robustness?—0
Dropping Pixels for Adversarial Robustness—0
Dual Manifold Adversarial Robustness: Defense against Lp and non-Lp Adversarial Attacks—0
Dynamical Low-Rank Compression of Neural Networks with Robustness under Adversarial Attacks—0
Dynamic Defense Approach for Adversarial Robustness in Deep Neural Networks via Stochastic Ensemble Smoothed Model—0
Dynamic ensemble selection based on Deep Neural Network Uncertainty Estimation for Adversarial Robustness—0
Dynamic Epsilon Scheduling: A Multi-Factor Adaptive Perturbation Budget for Adversarial Training—0
SegMix: Co-occurrence Driven Mixup for Semantic Segmentation and Adversarial Robustness—0
Adversarially Robust Neural Architectures—0
Edge-Only Universal Adversarial Attacks in Distributed Learning—0
Self-Knowledge Distillation via Dropout—0
Effective, Efficient and Robust Neural Architecture Search—0
Effects of Loss Functions And Target Representations on Adversarial Robustness—0
Efficiency-driven Hardware Optimization for Adversarially Robust Neural Networks—0
Intrinsic Biologically Plausible Adversarial Robustness—0
Efficient Certification for Probabilistic Robustness—0
Show:102550
← PrevPage 31 of 35Next →
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