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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 1001–1050 of 1746 papers

TitleStatusHype
SecPE: Secure Prompt Ensembling for Private and Robust Large Language Models—0
Secure Diagnostics: Adversarial Robustness Meets Clinical Interpretability—0
SAM Meets UAP: Attacking Segment Anything Model With Universal Adversarial Perturbation—0
SegMix: Co-occurrence Driven Mixup for Semantic Segmentation and Adversarial Robustness—0
Self-Knowledge Distillation via Dropout—0
Self-supervised Adversarial Robustness for the Low-label, High-data Regime—0
NCIS: Neural Contextual Iterative Smoothing for Purifying Adversarial Perturbations—0
Semantics-Preserving Adversarial Training—0
Semi-Implicit Hybrid Gradient Methods with Application to Adversarial Robustness—0
Semi-supervised Semantics-guided Adversarial Training for Trajectory Prediction—0
Sequential Bayesian Neural Subnetwork Ensembles—0
Sharp Statistical Guarantees for Adversarially Robust Gaussian Classification—0
ShieldNets: Defending Against Adversarial Attacks Using Probabilistic Adversarial Robustness—0
Shortcut Learning of Large Language Models in Natural Language Understanding—0
Singular Regularization with Information Bottleneck Improves Model's Adversarial Robustness—0
SMoA: Sparse Mixture of Adapters to Mitigate Multiple Dataset Biases—0
Smoothing Policy Iteration for Zero-sum Markov Games—0
Smooth Kernels Improve Adversarial Robustness and Perceptually-Aligned Gradients—0
SmoothMix: Training Confidence-calibrated Smoothed Classifiers for Certified Adversarial Robustness—0
Smoothness Analysis of Adversarial Training—0
SNEAK: Synonymous Sentences-Aware Adversarial Attack on Natural Language Video Localization—0
Soften to Defend: Towards Adversarial Robustness via Self-Guided Label Refinement—0
Sparse DNNs with Improved Adversarial Robustness—0
Spatial-Temporal-Fusion BNN: Variational Bayesian Feature Layer—0
SpeechGuard: Exploring the Adversarial Robustness of Multimodal Large Language Models—0
Speech Unlearning—0
SPLASH: Learnable Activation Functions for Improving Accuracy and Adversarial Robustness—0
SPROUT: Self-Progressing Robust Training—0
Standard-Deviation-Inspired Regularization for Improving Adversarial Robustness—0
STAR: Noisy Semi-Supervised Transfer Learning for Visual Classification—0
Stochastic Gradient Descent with Nonlinear Conjugate Gradient-Style Adaptive Momentum—0
Improving the Behaviour of Vision Transformers with Token-consistent Stochastic Layers—0
Stop Walking in Circles! Bailing Out Early in Projected Gradient Descent—0
StratDef: Strategic Defense Against Adversarial Attacks in ML-based Malware Detection—0
Strength-Adaptive Adversarial Training—0
Structural Extensions of Basis Pursuit: Guarantees on Adversarial Robustness—0
Structure-Preserving Progressive Low-rank Image Completion for Defending Adversarial Attacks—0
Struggle with Adversarial Defense? Try Diffusion—0
Survey of Adversarial Robustness in Multimodal Large Language Models—0
One-vs-the-Rest Loss to Focus on Important Samples in Adversarial Training—0
Sylva: Tailoring Personalized Adversarial Defense in Pre-trained Models via Collaborative Fine-tuning—0
Symmetry Defense Against CNN Adversarial Perturbation Attacks—0
Tail-aware Adversarial Attacks: A Distributional Approach to Efficient LLM Jailbreaking—0
Robust Adversarial Classification via Abstaining—0
TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models—0
Test-Time Adaptation and Adversarial Robustness—0
Test-Time Adaptation with Perturbation Consistency Learning—0
TETRIS: Towards Exploring the Robustness of Interactive Segmentation—0
The Attack Generator: A Systematic Approach Towards Constructing Adversarial Attacks—0
The Dilemma Between Data Transformations and Adversarial Robustness for Time Series Application Systems—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