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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 1301–1350 of 1746 papers

TitleStatusHype
Adversarial Robustness of Deep Neural Networks: A Survey from a Formal Verification Perspective—0
Towards Robust Vision Transformer via Masked Adaptive Ensemble—0
Reframing Neural Networks: Deep Structure in Overcomplete Representations—0
A Curious Case of Remarkable Resilience to Gradient Attacks via Fully Convolutional and Differentiable Front End with a Skip Connection—0
A Robust Defense against Adversarial Attacks on Deep Learning-based Malware Detectors via (De)Randomized Smoothing—0
Reinforced Compressive Neural Architecture Search for Versatile Adversarial Robustness—0
Reinforcing Adversarial Robustness using Model Confidence Induced by Adversarial Training—0
Relating Adversarially Robust Generalization to Flat Minima—0
Relationship between Model Compression and Adversarial Robustness: A Review of Current Evidence—0
Relaxing Graph Transformers for Adversarial Attacks—0
Releasing Inequality Phenomena in L_-Adversarial Training via Input Gradient Distillation—0
Adversarial Robustness of Deep Learning: Theory, Algorithms, and Applications—0
Reliable and Efficient Evaluation of Adversarial Robustness for Deep Hashing-Based Retrieval—0
Adversarial Robustness May Be at Odds With Simplicity—0
Towards Stable and Robust AdderNets—0
Removing Adversarial Noise in Class Activation Feature Space—0
Adversarial Robustness is at Odds with Lazy Training—0
Removing Out-of-Distribution Data Improves Adversarial Robustness—0
Towards Sustainable SecureML: Quantifying Carbon Footprint of Adversarial Machine Learning—0
XploreNAS: Explore Adversarially Robust & Hardware-efficient Neural Architectures for Non-ideal Xbars—0
Rerouting LLM Routers—0
Residual Error: a New Performance Measure for Adversarial Robustness—0
Resilience to Multiple Attacks via Adversarially Trained MIMO Ensembles—0
Revisiting and Advancing Adversarial Training Through A Simple Baseline—0
Adversarial Robustness in Unsupervised Machine Learning: A Systematic Review—0
Rethinking Audio-Visual Adversarial Vulnerability from Temporal and Modality Perspectives—0
Rethinking Classifier and Adversarial Attack—0
Towards the Memorization Effect of Neural Networks in Adversarial Training—0
Rethinking Feature Uncertainty in Stochastic Neural Networks for Adversarial Robustness—0
Rethinking Non-idealities in Memristive Crossbars for Adversarial Robustness in Neural Networks—0
Utilizing Adversarial Targeted Attacks to Boost Adversarial Robustness—0
Rethinking the Adversarial Robustness of Multi-Exit Neural Networks in an Attack-Defense Game—0
Towards Trustworthy and Aligned Machine Learning: A Data-centric Survey with Causality Perspectives—0
Adversarial Robustness in Two-Stage Learning-to-Defer: Algorithms and Guarantees—0
Revisiting Adversarial Robustness of Classifiers With a Reject Option—0
Adversarial Robustness in RGB-Skeleton Action Recognition: Leveraging Attention Modality Reweighter—0
Towards Understanding and Improving Adversarial Robustness of Vision Transformers—0
Adversarial Robustness in Parameter-Space Classifiers—0
Adversarial Robustness in Deep Learning: Attacks on Fragile Neurons—0
Adversarial Robustness Guarantees for Quantum Classifiers—0
Adversarial Robustness for Unsupervised Domain Adaptation—0
Revisiting Robustness in Graph Machine Learning—0
Revisiting Role of Autoencoders in Adversarial Settings—0
Revisiting Semi-supervised Adversarial Robustness via Noise-aware Online Robust Distillation—0
Revisiting the Adversarial Robustness-Accuracy Tradeoff in Robot Learning—0
Towards Understanding the Regularization of Adversarial Robustness on Neural Networks—0
Revisiting the Robust Generalization of Adversarial Prompt Tuning—0
Variance Reduced Halpern Iteration for Finite-Sum Monotone Inclusions—0
Towards unlocking the mystery of adversarial fragility of neural networks—0
Toward Transparent AI: A Survey on Interpreting the Inner Structures of Deep Neural Networks—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