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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 1251–1300 of 1746 papers

TitleStatusHype
Simple Post-Training Robustness Using Test Time Augmentations and Random ForestCode0
Adversarial Examples for Evaluating Math Word Problem SolversCode0
Adversarial Bone Length Attack on Action Recognition—0
2-in-1 Accelerator: Enabling Random Precision Switch for Winning Both Adversarial Robustness and Efficiency—0
Utilizing Adversarial Targeted Attacks to Boost Adversarial Robustness—0
Adversarial Robustness for Unsupervised Domain Adaptation—0
Impact of Attention on Adversarial Robustness of Image Classification Models—0
Sample Efficient Detection and Classification of Adversarial Attacks via Self-Supervised Embeddings—0
A Hierarchical Assessment of Adversarial SeverityCode0
Understanding the Logit Distributions of Adversarially-Trained Deep Neural Networks—0
Adversarially Robust One-class Novelty DetectionCode0
Bridged Adversarial Training—0
Adversarial Robustness of Deep Learning: Theory, Algorithms, and Applications—0
SegMix: Co-occurrence Driven Mixup for Semantic Segmentation and Adversarial Robustness—0
ASAT: Adaptively Scaled Adversarial Training in Time Series—0
Pruning in the Face of AdversariesCode0
STAR: Noisy Semi-Supervised Transfer Learning for Visual Classification—0
Neural Architecture Dilation for Adversarial Robustness—0
On the Effect of Pruning on Adversarial Robustness—0
Robust Transfer Learning with Pretrained Language Models through Adapters—0
Towards Adversarially Robust and Domain Generalizable Stereo Matching by Rethinking DNN Feature Backbones—0
Who's Afraid of Thomas Bayes?—0
Robust Explainability: A Tutorial on Gradient-Based Attribution Methods for Deep Neural Networks—0
AdvFilter: Predictive Perturbation-aware Filtering against Adversarial Attack via Multi-domain Learning—0
Perceptual-based deep-learning denoiser as a defense against adversarial attacks on ASR systems—0
A Closer Look at the Adversarial Robustness of Information Bottleneck Models—0
Improving Model Robustness with Latent Distribution Locally and GloballyCode0
Understanding Intrinsic Robustness Using Label UncertaintyCode0
Generalization Error Analysis of Neural networks with Gradient Based Regularization—0
GradDiv: Adversarial Robustness of Randomized Neural Networks via Gradient Diversity Regularization—0
Adversarial Robustness of Probabilistic Network Embedding for Link Prediction—0
Adversarial Robustness of Streaming Algorithms through Importance Sampling—0
Multi-stage Optimization based Adversarial Training—0
On the (Un-)Avoidability of Adversarial Examples—0
On Adversarial Robustness of Synthetic Code Generation—0
NCIS: Neural Contextual Iterative Smoothing for Purifying Adversarial Perturbations—0
Long-term Cross Adversarial Training: A Robust Meta-learning Method for Few-shot Classification TasksCode0
Policy Smoothing for Provably Robust Reinforcement Learning—0
Residual Error: a New Performance Measure for Adversarial Robustness—0
Less is More: Feature Selection for Adversarial Robustness with Compressive Counter-Adversarial Attacks—0
Attacking Graph Classification via Bayesian Optimisation—0
Analyzing Adversarial Robustness of Deep Neural Networks in Pixel Space: a Semantic Perspective—0
SmoothMix: Training Confidence-calibrated Smoothed Classifiers for Certified Adversarial Robustness—0
Algorithmic Bias and Data Bias: Understanding the Relation between Distributionally Robust Optimization and Data Curation—0
Towards Adversarial Robustness via Transductive Learning—0
ATRAS: Adversarially Trained Robust Architecture Search—0
Understanding the Interplay between Privacy and Robustness in Federated Learning—0
CARTL: Cooperative Adversarially-Robust Transfer LearningCode0
Adversarial Robustness via Fisher-Rao RegularizationCode0
Corruption-Robust Offline Reinforcement Learning—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