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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 1451–1500 of 1746 papers

TitleStatusHype
CSTAR: Towards Compact and STructured Deep Neural Networks with Adversarial Robustness—0
Curvature Dynamic Black-box Attack: revisiting adversarial robustness via dynamic curvature estimation—0
RoMA: Robust Malware Attribution via Byte-level Adversarial Training with Global Perturbations and Adversarial Consistency Regularization—0
DART: A Principled Approach to Adversarially Robust Unsupervised Domain Adaptation—0
Data-Driven Lipschitz Continuity: A Cost-Effective Approach to Improve Adversarial Robustness—0
RoSearch: Search for Robust Student Architectures When Distilling Pre-trained Language Models—0
When is dataset cartography ineffective? Using training dynamics does not improve robustness against Adversarial SQuAD—0
DataFreeShield: Defending Adversarial Attacks without Training Data—0
Mining Data Impressions from Deep Models as Substitute for the Unavailable Training Data—0
Absum: Simple Regularization Method for Reducing Structural Sensitivity of Convolutional Neural Networks—0
Adversarially Robust Streaming Algorithms via Differential Privacy—0
RUSH: Robust Contrastive Learning via Randomized Smoothing—0
Deadwooding: Robust Global Pruning for Deep Neural Networks—0
SafeGenes: Evaluating the Adversarial Robustness of Genomic Foundation Models—0
LLM Safeguard is a Double-Edged Sword: Exploiting False Positives for Denial-of-Service Attacks—0
Deciphering the Definition of Adversarial Robustness for post-hoc OOD Detectors—0
CausalPC: Improving the Robustness of Point Cloud Classification by Causal Effect Identification—0
DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models—0
Causal Information Bottleneck Boosts Adversarial Robustness of Deep Neural Network—0
Causal Feature Selection for Responsible Machine Learning—0
Deep Adversarial Defense Against Multilevel-Lp Attacks—0
Sample Complexity of Adversarially Robust Linear Classification on Separated Data—0
Sample Efficient Detection and Classification of Adversarial Attacks via Self-Supervised Embeddings—0
Scalable Lipschitz Estimation for CNNs—0
Deep Learning Training Procedure Augmentations—0
Deep Repulsive Prototypes for Adversarial Robustness—0
DeepSafe: A Data-driven Approach for Checking Adversarial Robustness in Neural Networks—0
Adversary Agnostic Robust Deep Reinforcement Learning—0
Scalable Neural Learning for Verifiable Consistency with Temporal Specifications—0
Defending Against Adversarial Examples by Regularized Deep Embedding—0
Defending against Adversarial Malware Attacks on ML-based Android Malware Detection Systems—0
Defending Against Multiple and Unforeseen Adversarial Videos—0
Defending From Physically-Realizable Adversarial Attacks Through Internal Over-Activation Analysis—0
Causal Fair Metric: Bridging Causality, Individual Fairness, and Adversarial Robustness—0
Defense-PointNet: Protecting PointNet Against Adversarial Attacks—0
Defense Through Diverse Directions—0
Delving into Decision-based Black-box Attacks on Semantic Segmentation—0
Delving into Feature Space: Improving Adversarial Robustness by Feature Spectral Regularization—0
Delving into the Adversarial Robustness of Federated Learning—0
Causal Adversarial Perturbations for Individual Fairness and Robustness in Heterogeneous Data Spaces—0
Scalable Quantitative Verification For Deep Neural Networks—0
Demotivate adversarial defense in remote sensing—0
Catastrophic Overfitting: A Potential Blessing in Disguise—0
CARE: Ensemble Adversarial Robustness Evaluation Against Adaptive Attackers for Security Applications—0
Scalable Whitebox Attacks on Tree-based Models—0
CAP-GAN: Towards Adversarial Robustness with Cycle-consistent Attentional Purification—0
Denoising Autoencoder-based Defensive Distillation as an Adversarial Robustness Algorithm—0
Visual Interpretability Alone Helps Adversarial Robustness—0
DensePure: Understanding Diffusion Models towards Adversarial Robustness—0
Adversarially Robust Spiking Neural Networks with Sparse Connectivity—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