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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 751–800 of 1746 papers

TitleStatusHype
Constrained Adaptive Attacks: Realistic Evaluation of Adversarial Examples and Robust Training of Deep Neural Networks for Tabular Data—0
Measuring Adversarial Datasets—0
Causal Fair Metric: Bridging Causality, Individual Fairness, and Adversarial Robustness—0
Deep anytime-valid hypothesis testingCode0
Detection Defenses: An Empty Promise against Adversarial Patch Attacks on Optical FlowCode0
Deceptive Fairness Attacks on Graphs via Meta LearningCode0
Semantic-Aware Adversarial Training for Reliable Deep Hashing RetrievalCode0
F^2AT: Feature-Focusing Adversarial Training via Disentanglement of Natural and Perturbed Patterns—0
SAM Meets UAP: Attacking Segment Anything Model With Universal Adversarial Perturbation—0
On existence, uniqueness and scalability of adversarial robustness measures for AI classifiers—0
Adversarial Robustness Unhardening via Backdoor Attacks in Federated Learning—0
A Non-monotonic Smooth Activation Function—0
Promoting Robustness of Randomized Smoothing: Two Cost-Effective Approaches—0
A Geometrical Approach to Evaluate the Adversarial Robustness of Deep Neural Networks—0
Investigating the Adversarial Robustness of Density Estimation Using the Probability Flow ODE—0
PAC-Bayesian Spectrally-Normalized Bounds for Adversarially Robust Generalization—0
Enhancing Robust Representation in Adversarial Training: Alignment and Exclusion CriteriaCode0
Variance Reduced Halpern Iteration for Finite-Sum Monotone Inclusions—0
Can Language Models be Instructed to Protect Personal Information?—0
AutoLoRa: A Parameter-Free Automated Robust Fine-Tuning Framework—0
Understanding Robust Overfitting from the Feature Generalization Perspective—0
Adversarial Machine Learning in Latent Representations of Neural NetworksCode0
Intrinsic Biologically Plausible Adversarial Robustness—0
On the Trade-offs between Adversarial Robustness and Actionable Explanations—0
Adversarial Examples Might be Avoidable: The Role of Data Concentration in Adversarial Robustness—0
Projected Randomized Smoothing for Certified Adversarial RobustnessCode0
Improving Robustness of Deep Convolutional Neural Networks via Multiresolution Learning—0
RBFormer: Improve Adversarial Robustness of Transformer by Robust Bias—0
VIC-KD: Variance-Invariance-Covariance Knowledge Distillation to Make Keyword Spotting More Robust Against Adversarial Attacks—0
On the Relationship between Skill Neurons and Robustness in Prompt TuningCode0
Language Guided Adversarial PurificationCode0
Evaluating Adversarial Robustness with Expected Viable Performance—0
DAD++: Improved Data-free Test Time Adversarial DefenseCode0
Exploring Robust Features for Improving Adversarial Robustness—0
Regret-Optimal Federated Transfer Learning for Kernel Regression with Applications in American Option PricingCode0
Adversarially Robust Learning with Optimal Transport Regularized DivergencesCode0
J-Guard: Journalism Guided Adversarially Robust Detection of AI-generated NewsCode0
RobustEdge: Low Power Adversarial Detection for Cloud-Edge Systems—0
Advancing Adversarial Robustness Through Adversarial Logit Update—0
Prediction without Preclusion: Recourse Verification with Reachable SetsCode0
Fast Adversarial Training with Smooth ConvergenceCode0
Don't Look into the Sun: Adversarial Solarization Attacks on Image ClassifiersCode0
Measuring the Effect of Causal Disentanglement on the Adversarial Robustness of Neural Network Models—0
Causal Adversarial Perturbations for Individual Fairness and Robustness in Heterogeneous Data Spaces—0
Benchmarking Adversarial Robustness of Compressed Deep Learning Models—0
Expressivity of Graph Neural Networks Through the Lens of Adversarial RobustnessCode0
Enhancing the Antidote: Improved Pointwise Certifications against Poisoning Attacks—0
On the Interplay of Convolutional Padding and Adversarial RobustnessCode0
ModSec-AdvLearn: Countering Adversarial SQL Injections with Robust Machine LearningCode0
Improving Performance of Semi-Supervised Learning by Adversarial Attacks—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