Adversarial Robustness

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

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 1426–1450 of 1746 papers

Title	Date	Tasks	Status
Collective Robustness Certificates	Jan 1, 2021	Adversarial RobustnessImage Segmentation	—Unverified
Collective Robustness Certificates: Exploiting Interdependence in Graph Neural Networks	Feb 6, 2023	Adversarial RobustnessImage Segmentation	—Unverified
Certified Adversarial Robustness via Anisotropic Randomized Smoothing	Jul 12, 2022	Adversarial RobustnessAll	—Unverified
Complexity Matters: Effective Dimensionality as a Measure for Adversarial Robustness	Oct 24, 2024	Adversarial RobustnessModel Selection	—Unverified
Certified Adversarial Robustness Under the Bounded Support Set	Sep 29, 2021	Adversarial Robustness	—Unverified
Robust Survival Analysis with Adversarial Regularization	Dec 26, 2023	Adversarial RobustnessSurvival Analysis	—Unverified
Robust Tickets Can Transfer Better: Drawing More Transferable Subnetworks in Transfer Learning	Apr 24, 2023	Adversarial RobustnessTransfer Learning	—Unverified
Robust Transferable Feature Extractors: Learning to Defend Pre-Trained Networks Against White Box Adversaries	Sep 14, 2022	Adversarial Robustness	—Unverified
Robust Transfer Learning with Pretrained Language Models through Adapters	Aug 5, 2021	Adversarial AttackAdversarial Robustness	—Unverified
Robust Unsupervised Domain Adaptation for 3D Point Cloud Segmentation Under Source Adversarial Attacks	Apr 2, 2025	Adversarial RobustnessDecoder	—Unverified
Conflict-Aware Adversarial Training	Oct 21, 2024	Adversarial Robustness	—Unverified
Confronting the Reproducibility Crisis: A Case Study of Challenges in Cybersecurity AI	May 29, 2024	Adversarial Robustness	—Unverified
Certified Adversarial Robustness of Machine Learning-based Malware Detectors via (De)Randomized Smoothing	May 1, 2024	Adversarial RobustnessMalware Detection	—Unverified
Certifiable Robustness to Adversarial State Uncertainty in Deep Reinforcement Learning	Apr 11, 2020	Adversarial RobustnessCollision Avoidance	—Unverified
Transgressing the boundaries: towards a rigorous understanding of deep learning and its (non-)robustness	Jul 5, 2023	Adversarial RobustnessLearning Theory	—Unverified
Certified Adversarial Robustness for Deep Reinforcement Learning	Oct 28, 2019	Adversarial RobustnessCollision Avoidance	—Unverified
Constrained Adaptive Attacks: Realistic Evaluation of Adversarial Examples and Robust Training of Deep Neural Networks for Tabular Data	Nov 8, 2023	Adversarial Robustness	—Unverified
Constrained Learning with Non-Convex Losses	Mar 8, 2021	Adversarial RobustnessFairness	—Unverified
Constraining Logits by Bounded Function for Adversarial Robustness	Oct 6, 2020	Adversarial Robustness	—Unverified
Contextual Fusion For Adversarial Robustness	Nov 18, 2020	Adversarial Robustness	—Unverified
Corruption-Robust Offline Reinforcement Learning	Jun 11, 2021	Adversarial RobustnessOffline RL	—Unverified
Certified Adversarial Defenses Meet Out-of-Distribution Corruptions: Benchmarking Robustness and Simple Baselines	Dec 1, 2021	Adversarial RobustnessBenchmarking	—Unverified
Criticality Leveraged Adversarial Training (CLAT) for Boosted Performance via Parameter Efficiency	Aug 19, 2024	Adversarial Robustness	—Unverified
Cross Domain Generative Augmentation: Domain Generalization with Latent Diffusion Models	Dec 8, 2023	Adversarial RobustnessData Augmentation	—Unverified
Cross-Entropy Loss Functions: Theoretical Analysis and Applications	Apr 14, 2023	Adversarial Robustness	—Unverified

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All datasets AdvGLUE CIFAR-10 ImageNet ImageNet-A ImageNet-C Stylized ImageNet CIFAR-100

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	DeBERTa (single model)	Accuracy	0.61	—	Unverified
2	ALBERT (single model)	Accuracy	0.59	—	Unverified
3	T5 (single model)	Accuracy	0.57	—	Unverified
4	SMART_RoBERTa (single model)	Accuracy	0.54	—	Unverified
5	FreeLB (single model)	Accuracy	0.5	—	Unverified
6	RoBERTa (single model)	Accuracy	0.5	—	Unverified
7	InfoBERT (single model)	Accuracy	0.46	—	Unverified
8	ELECTRA (single model)	Accuracy	0.42	—	Unverified
9	BERT (single model)	Accuracy	0.34	—	Unverified
10	SMART_BERT (single model)	Accuracy	0.3	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Mixed classifier	Accuracy	95.23	—	Unverified
2	Stochastic-LWTA/PGD/WideResNet-34-10	Accuracy	92.26	—	Unverified
3	Stochastic-LWTA/PGD/WideResNet-34-5	Accuracy	91.88	—	Unverified
4	GLOT-DR	Accuracy	84.13	—	Unverified
5	TRADES-ANCRA/ResNet18	Accuracy	81.7	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ResNet-50 (SGD, Cosine)	Accuracy	77.4	—	Unverified
2	ResNet-50 (SGD, Step)	Accuracy	76.9	—	Unverified
3	DeiT-S (AdamW, Cosine)	Accuracy	76.8	—	Unverified
4	ResNet-50 (AdamW, Cosine)	Accuracy	76.4	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	DeiT-S (AdamW, Cosine)	Accuracy	12.2	—	Unverified
2	ResNet-50 (SGD, Cosine)	Accuracy	3.3	—	Unverified
3	ResNet-50 (SGD, Step)	Accuracy	3.2	—	Unverified
4	ResNet-50 (AdamW, Cosine)	Accuracy	3.1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ResNet-50 (AdamW, Cosine)	mean Corruption Error (mCE)	59.3	—	Unverified
2	ResNet-50 (SGD, Step)	mean Corruption Error (mCE)	57.9	—	Unverified
3	ResNet-50 (SGD, Cosine)	mean Corruption Error (mCE)	56.9	—	Unverified
4	DeiT-S (AdamW, Cosine)	mean Corruption Error (mCE)	48	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	DeiT-S (AdamW, Cosine)	Accuracy	13	—	Unverified
2	ResNet-50 (SGD, Cosine)	Accuracy	8.4	—	Unverified
3	ResNet-50 (SGD, Step)	Accuracy	8.3	—	Unverified
4	ResNet-50 (AdamW, Cosine)	Accuracy	8.1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Mixed Classifier	Clean Accuracy	85.21	—	Unverified
2	ResNet18/MART-ANCRA	Clean Accuracy	60.1	—	Unverified