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 1251–1275 of 1746 papers

Title	Date	Tasks	Status
I Can Find You in Seconds! Leveraging Large Language Models for Code Authorship Attribution	Jan 14, 2025	Adversarial RobustnessAttribute	—Unverified
IDEA: Invariant Defense for Graph Adversarial Robustness	May 25, 2023	Adversarial Robustness	—Unverified
Illusory Attacks: Information-Theoretic Detectability Matters in Adversarial Attacks	Jul 20, 2022	Adversarial AttackAdversarial Robustness	—Unverified
Imbalanced Gradients: A New Cause of Overestimated Adversarial Robustness	Sep 28, 2020	Adversarial Robustness	—Unverified
Impact of Attention on Adversarial Robustness of Image Classification Models	Sep 2, 2021	Adversarial RobustnessClassification	—Unverified
Impact of Low-bitwidth Quantization on the Adversarial Robustness for Embedded Neural Networks	Sep 27, 2019	Adversarial RobustnessBIG-bench Machine Learning	—Unverified
Impact of Spatial Frequency Based Constraints on Adversarial Robustness	Apr 26, 2021	Adversarial Robustness	—Unverified
Imperceptible Adversarial Attacks on Point Clouds Guided by Point-to-Surface Field	Dec 26, 2024	Adversarial RobustnessAttribute	—Unverified
Implicit Euler Skip Connections: Enhancing Adversarial Robustness via Numerical Stability	Jan 1, 2020	Adversarial AttackAdversarial Robustness	—Unverified
Improve Adversarial Robustness via Weight Penalization on Classification Layer	Oct 8, 2020	Adversarial RobustnessClassification	—Unverified
Improved Adversarial Robustness by Reducing Open Space Risk via Tent Activations	Aug 7, 2019	Adversarial RobustnessBIG-bench Machine Learning	—Unverified
Improved Adversarial Robustness via Logit Regularization Methods	Jun 10, 2019	Adversarial DefenseAdversarial Robustness	—Unverified
Improved Branch and Bound for Neural Network Verification via Lagrangian Decomposition	Apr 14, 2021	Adversarial RobustnessGPU	—Unverified
MadNet: Using a MAD Optimization for Defending Against Adversarial Attacks	Nov 3, 2019	Adversarial DefenseAdversarial Robustness	—Unverified
Improve Generalization and Robustness of Neural Networks via Weight Scale Shifting Invariant Regularizations	Aug 7, 2020	Adversarial Robustness	—Unverified
Improving Adversarial Robustness by Encouraging Discriminative Features	Nov 1, 2018	Adversarial Robustness	—Unverified
Improving Adversarial Robustness by Contrastive Guided Diffusion Process	Oct 18, 2022	Adversarial RobustnessSynthetic Data Generation	—Unverified
Improving Adversarial Robustness for 3D Point Cloud Recognition at Test-Time through Purified Self-Training	Sep 23, 2024	Adversarial PurificationAdversarial Robustness	—Unverified
Improving Adversarial Robustness for Free with Snapshot Ensemble	Oct 7, 2021	Adversarial Robustness	—Unverified
Improving Adversarial Robustness in Weight-quantized Neural Networks	Dec 29, 2020	Adversarial RobustnessQuantization	—Unverified
Improving adversarial robustness of deep neural networks by using semantic information	Aug 18, 2020	Adversarial AttackAdversarial Robustness	—Unverified
Improving Adversarial Robustness of Ensembles with Diversity Training	Jan 28, 2019	Adversarial RobustnessDiversity	—Unverified
SAT: Improving Adversarial Training via Curriculum-Based Loss Smoothing	Mar 18, 2020	Adversarial Robustness	—Unverified
Improving Adversarial Robustness to Sensitivity and Invariance Attacks with Deep Metric Learning	Nov 4, 2022	Adversarial RobustnessMetric Learning	—Unverified
Improving Adversarial Robustness via Attention and Adversarial Logit Pairing	Aug 23, 2019	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