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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 801–850 of 1746 papers

TitleStatusHype
Enhance DNN Adversarial Robustness and Efficiency via Injecting Noise to Non-Essential Neurons—0
Harmonizing Feature Maps: A Graph Convolutional Approach for Enhancing Adversarial Robustness—0
A Robust Adversarial Ensemble with Causal (Feature Interaction) Interpretations for Image Classification—0
Improving Robustness with Image Filtering—0
Heterogeneous Architecture Search Approach within Adversarial Dynamic Defense Framework—0
Hierarchical Binding in Convolutional Neural Networks Confers Adversarial Robustness—0
Empirical Study of the Decision Region and Robustness in Deep Neural Networks—0
Adversarial Robustness via Label-Smoothing—0
Emoti-Attack: Zero-Perturbation Adversarial Attacks on NLP Systems via Emoji Sequences—0
_1 Adversarial Robustness Certificates: a Randomized Smoothing Approach—0
Are Time-Series Foundation Models Deployment-Ready? A Systematic Study of Adversarial Robustness Across Domains—0
Improving Calibration through the Relationship with Adversarial Robustness—0
Holistic Adversarial Robustness of Deep Learning Models—0
Eight challenges in developing theory of intelligence—0
Are Perceptually-Aligned Gradients a General Property of Robust Classifiers?—0
Improving Robustness Against Adversarial Attacks with Deeply Quantized Neural Networks—0
Are models trained on temporally-continuous data streams more adversarially robust?—0
On Transfer of Adversarial Robustness from Pretraining to Downstream Tasks—0
How and When Adversarial Robustness Transfers in Knowledge Distillation?—0
How benign is benign overfitting?—0
How Benign is Benign Overfitting ?—0
How Do Diffusion Models Improve Adversarial Robustness?—0
How do SGD hyperparameters in natural training affect adversarial robustness?—0
Towards Adversarially Robust Recommendation from Adaptive Fraudster Detection—0
Improving Performance of Semi-Supervised Learning by Adversarial Attacks—0
How Robust are Randomized Smoothing based Defenses to Data Poisoning?—0
Adversarial Robustness in Deep Learning: Attacks on Fragile Neurons—0
Efficient Certification for Probabilistic Robustness—0
How to beat a Bayesian adversary—0
How to Enhance Downstream Adversarial Robustness (almost) without Touching the Pre-Trained Foundation Model?—0
A3T: Adversarially Augmented Adversarial Training—0
Improving Robust Generalization by Direct PAC-Bayesian Bound Minimization—0
Improving Robustness of Deep Convolutional Neural Networks via Multiresolution Learning—0
Improving White-box Robustness of Pre-processing Defenses via Joint Adversarial Training—0
Intrinsic Biologically Plausible Adversarial Robustness—0
Efficiency-driven Hardware Optimization for Adversarially Robust Neural Networks—0
Hybrid Deep Learning Model using SPCAGAN Augmentation for Insider Threat Analysis—0
Hydra: An Agentic Reasoning Approach for Enhancing Adversarial Robustness and Mitigating Hallucinations in Vision-Language Models—0
Improving the Adversarial Robustness for Speaker Verification by Self-Supervised Learning—0
Effects of Loss Functions And Target Representations on Adversarial Robustness—0
Adversarially Robust Spiking Neural Networks with Sparse Connectivity—0
Effective, Efficient and Robust Neural Architecture Search—0
Improving Hyperspectral Adversarial Robustness Under Multiple Attacks—0
Adversarial Robustness Guarantees for Quantum Classifiers—0
I Can Find You in Seconds! Leveraging Large Language Models for Code Authorship Attribution—0
IDEA: Invariant Defense for Graph Adversarial Robustness—0
Edge-Only Universal Adversarial Attacks in Distributed Learning—0
Improving Graph Neural Networks via Adversarial Robustness Evaluation—0
Improving Generalization of Adversarial Training via Robust Critical Fine-Tuning—0
Are classical deep neural networks weakly adversarially robust?—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