SOTAVerified

Adversarial Robustness

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

Papers

Showing 601650 of 1746 papers

TitleStatusHype
Towards Reliable Evaluation and Fast Training of Robust Semantic Segmentation ModelsCode1
Towards quantum enhanced adversarial robustness in machine learning0
Anticipatory Thinking Challenges in Open Worlds: Risk Management0
Physics-constrained Attack against Convolution-based Human Motion PredictionCode0
A Comprehensive Study on the Robustness of Image Classification and Object Detection in Remote Sensing: Surveying and Benchmarking0
DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models0
Eight challenges in developing theory of intelligence0
BNN-DP: Robustness Certification of Bayesian Neural Networks via Dynamic ProgrammingCode0
Adversarial Robustness of Prompt-based Few-Shot Learning for Natural Language UnderstandingCode0
Revisiting and Advancing Adversarial Training Through A Simple Baseline0
AROID: Improving Adversarial Robustness Through Online Instance-Wise Data AugmentationCode0
NeRFool: Uncovering the Vulnerability of Generalizable Neural Radiance Fields against Adversarial PerturbationsCode1
Boosting Adversarial Robustness using Feature Level Stochastic SmoothingCode0
Expanding Scope: Adapting English Adversarial Attacks to ChineseCode0
Faithful Knowledge Distillation0
Transferable Adversarial Robustness for Categorical Data via Universal Robust EmbeddingsCode0
Revisiting the Trade-off between Accuracy and Robustness via Weight Distribution of FiltersCode0
Adversarial alignment: Breaking the trade-off between the strength of an attack and its relevance to human perception0
Evaluating robustness of support vector machines with the Lagrangian dual approach0
A Closer Look at the Adversarial Robustness of Deep Equilibrium ModelsCode0
Improving Adversarial Robustness of DEQs with Explicit Regulations Along the Neural DynamicsCode0
Multi-Objective Population Based TrainingCode1
Robust low-rank training via approximate orthonormal constraints0
Adversarial Robustness in Unsupervised Machine Learning: A Systematic Review0
Red Teaming Language Model Detectors with Language ModelsCode1
Exploiting Explainability to Design Adversarial Attacks and Evaluate Attack Resilience in Hate-Speech Detection Models0
Backdoor Attacks Against Incremental Learners: An Empirical Evaluation Study0
Two Heads are Better than One: Towards Better Adversarial Robustness by Combining Transduction and Rejection0
On the Importance of Backbone to the Adversarial Robustness of Object DetectorsCode0
On Evaluating Adversarial Robustness of Large Vision-Language ModelsCode2
Carefully Blending Adversarial Training, Purification, and Aggregation Improves Adversarial RobustnessCode0
Don't Retrain, Just Rewrite: Countering Adversarial Perturbations by Rewriting Text0
IDEA: Invariant Defense for Graph Adversarial Robustness0
Robust Classification via a Single Diffusion ModelCode1
AdvFunMatch: When Consistent Teaching Meets Adversarial Robustness0
Non-adversarial Robustness of Deep Learning Methods for Computer Vision0
Adversarial robustness of amortized Bayesian inferenceCode0
Expressive Losses for Verified Robustness via Convex CombinationsCode0
Decoupled Kullback-Leibler Divergence LossCode1
DeepBern-Nets: Taming the Complexity of Certifying Neural Networks using Bernstein Polynomial Activations and Precise Bound PropagationCode0
Annealing Self-Distillation Rectification Improves Adversarial TrainingCode0
Adversarial Amendment is the Only Force Capable of Transforming an Enemy into a Friend0
Quantifying the robustness of deep multispectral segmentation models against natural perturbations and data poisoningCode3
Raising the Bar for Certified Adversarial Robustness with Diffusion Models0
Variational ClassificationCode0
Iterative Adversarial Attack on Image-guided Story Ending Generation0
Releasing Inequality Phenomena in L_-Adversarial Training via Input Gradient Distillation0
Watermarking Text Generated by Black-Box Language ModelsCode1
Stochastic Security as a Performance Metric for Quantum-enhanced Generative AI0
Physical-layer Adversarial Robustness for Deep Learning-based Semantic Communications0
Show:102550
← PrevPage 13 of 35Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DeBERTa (single model)Accuracy0.61Unverified
2ALBERT (single model)Accuracy0.59Unverified
3T5 (single model)Accuracy0.57Unverified
4SMART_RoBERTa (single model)Accuracy0.54Unverified
5FreeLB (single model)Accuracy0.5Unverified
6RoBERTa (single model)Accuracy0.5Unverified
7InfoBERT (single model)Accuracy0.46Unverified
8ELECTRA (single model)Accuracy0.42Unverified
9BERT (single model)Accuracy0.34Unverified
10SMART_BERT (single model)Accuracy0.3Unverified
#ModelMetricClaimedVerifiedStatus
1Mixed classifierAccuracy95.23Unverified
2Stochastic-LWTA/PGD/WideResNet-34-10Accuracy92.26Unverified
3Stochastic-LWTA/PGD/WideResNet-34-5Accuracy91.88Unverified
4GLOT-DRAccuracy84.13Unverified
5TRADES-ANCRA/ResNet18Accuracy81.7Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet-50 (SGD, Cosine)Accuracy77.4Unverified
2ResNet-50 (SGD, Step)Accuracy76.9Unverified
3DeiT-S (AdamW, Cosine)Accuracy76.8Unverified
4ResNet-50 (AdamW, Cosine)Accuracy76.4Unverified
#ModelMetricClaimedVerifiedStatus
1DeiT-S (AdamW, Cosine)Accuracy12.2Unverified
2ResNet-50 (SGD, Cosine)Accuracy3.3Unverified
3ResNet-50 (SGD, Step)Accuracy3.2Unverified
4ResNet-50 (AdamW, Cosine)Accuracy3.1Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet-50 (AdamW, Cosine)mean Corruption Error (mCE)59.3Unverified
2ResNet-50 (SGD, Step)mean Corruption Error (mCE)57.9Unverified
3ResNet-50 (SGD, Cosine)mean Corruption Error (mCE)56.9Unverified
4DeiT-S (AdamW, Cosine)mean Corruption Error (mCE)48Unverified
#ModelMetricClaimedVerifiedStatus
1DeiT-S (AdamW, Cosine)Accuracy13Unverified
2ResNet-50 (SGD, Cosine)Accuracy8.4Unverified
3ResNet-50 (SGD, Step)Accuracy8.3Unverified
4ResNet-50 (AdamW, Cosine)Accuracy8.1Unverified
#ModelMetricClaimedVerifiedStatus
1Mixed ClassifierClean Accuracy85.21Unverified
2ResNet18/MART-ANCRAClean Accuracy60.1Unverified