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 1126–1150 of 1746 papers

Title	Date	Tasks	Status
Understanding Adversarial Robustness Through Loss Landscape Geometries	Jul 22, 2019	Adversarial RobustnessData Augmentation	—Unverified
Understanding and Measuring Robustness of Multimodal Learning	Dec 22, 2021	Adversarial Robustness	—Unverified
Understanding the Impact of Graph Reduction on Adversarial Robustness in Graph Neural Networks	Dec 8, 2024	Adversarial Robustness	—Unverified
Understanding the Interplay between Privacy and Robustness in Federated Learning	Jun 13, 2021	Adversarial RobustnessFederated Learning	—Unverified
Understanding the Logit Distributions of Adversarially-Trained Deep Neural Networks	Aug 26, 2021	Adversarial Robustness	—Unverified
Universal Adversarial Framework to Improve Adversarial Robustness for Diabetic Retinopathy Detection	Dec 13, 2023	Adversarial RobustnessDiabetic Retinopathy Detection	—Unverified
Universal Adversarial Training with Class-Wise Perturbations	Apr 7, 2021	Adversarial Robustness	—Unverified
Classifier-independent Lower-Bounds for Adversarial Robustness	Jun 17, 2020	Adversarial AttackAdversarial Robustness	—Unverified
Universally Robust Graph Neural Networks by Preserving Neighbor Similarity	Jan 18, 2024	Adversarial Robustness	—Unverified
Unlabeled Data Help: Minimax Analysis and Adversarial Robustness	Feb 14, 2022	Adversarial RobustnessSelf-Supervised Learning	—Unverified
Unpacking Robustness in Inflectional Languages: Adversarial Evaluation and Mechanistic Insights	May 8, 2025	Adversarial Robustness	—Unverified
Unreasonable Effectiveness of Last Hidden Layer Activations for Adversarial Robustness	Feb 15, 2022	Adversarial Robustness	—Unverified
Unrevealed Threats: A Comprehensive Study of the Adversarial Robustness of Underwater Image Enhancement Models	Sep 10, 2024	Adversarial AttackAdversarial Robustness	—Unverified
Unsupervised Adversarially-Robust Representation Learning on Graphs	Dec 4, 2020	Adversarial RobustnessCommunity Detection	—Unverified
Unveiling Project-Specific Bias in Neural Code Models	Jan 19, 2022	Adversarial RobustnessVulnerability Detection	—Unverified
Unveiling the Role of Randomization in Multiclass Adversarial Classification: Insights from Graph Theory	Mar 18, 2025	Adversarial RobustnessBinary Classification	—Unverified
Use of small auxiliary networks and scarce data to improve the adversarial robustness of deep learning models	Sep 29, 2021	Adversarial Robustnessimage-classification	—Unverified
Local Convolutions Cause an Implicit Bias towards High Frequency Adversarial Examples	Jun 19, 2020	Adversarial RobustnessVocal Bursts Intensity Prediction	—Unverified
Utilizing Adversarial Targeted Attacks to Boost Adversarial Robustness	Sep 4, 2021	Adversarial AttackAdversarial Robustness	—Unverified
Variance Reduced Halpern Iteration for Finite-Sum Monotone Inclusions	Oct 4, 2023	Adversarial Robustness	—Unverified
Variational Autoencoders: A Harmonic Perspective	May 31, 2021	Adversarial RobustnessDecoder	—Unverified
Variational Randomized Smoothing for Sample-Wise Adversarial Robustness	Jul 16, 2024	Adversarial Robustness	—Unverified
VIC-KD: Variance-Invariance-Covariance Knowledge Distillation to Make Keyword Spotting More Robust Against Adversarial Attacks	Sep 22, 2023	Adversarial RobustnessKeyword Spotting	—Unverified
Visual Interpretability Alone Helps Adversarial Robustness	Sep 25, 2019	Adversarial Robustness	—Unverified
VQAttack: Transferable Adversarial Attacks on Visual Question Answering via Pre-trained Models	Feb 16, 2024	Adversarial RobustnessLanguage Modelling	—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