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 1551–1575 of 1746 papers

Title	Date	Tasks	Status
VQAttack: Transferable Adversarial Attacks on Visual Question Answering via Pre-trained Models	Feb 16, 2024	Adversarial RobustnessLanguage Modelling	—Unverified
Self-supervised Adversarial Robustness for the Low-label, High-data Regime	Jan 1, 2021	Adversarial RobustnessSelf-Supervised Learning	—Unverified
CAMUS: A Framework to Build Formal Specifications for Deep Perception Systems Using Simulators	Nov 25, 2019	Adversarial Robustness	—Unverified
Calibration and Consistency of Adversarial Surrogate Losses	Apr 19, 2021	Adversarial Robustness	—Unverified
Buffer Zone based Defense against Adversarial Examples in Image Classification	Jan 1, 2021	Adversarial RobustnessClassification	—Unverified
Adversarially Robust Industrial Anomaly Detection Through Diffusion Model	Aug 9, 2024	Adversarial PurificationAdversarial Robustness	—Unverified
Eight challenges in developing theory of intelligence	Jun 20, 2023	Adversarial RobustnessContinual Learning	—Unverified
Bridged Adversarial Training	Aug 25, 2021	Adversarial Robustness	—Unverified
_1 Adversarial Robustness Certificates: a Randomized Smoothing Approach	Sep 25, 2019	Adversarial Robustness	—Unverified
Emoti-Attack: Zero-Perturbation Adversarial Attacks on NLP Systems via Emoji Sequences	Feb 24, 2025	Adversarial AttackAdversarial Robustness	—Unverified
Empirical Study of the Decision Region and Robustness in Deep Neural Networks	Sep 29, 2021	Adversarial AttackAdversarial Robustness	—Unverified
NCIS: Neural Contextual Iterative Smoothing for Purifying Adversarial Perturbations	Jun 22, 2021	Adversarial DefenseAdversarial Robustness	—Unverified
Training Graph Neural Networks Using Non-Robust Samples	Dec 19, 2024	Adversarial RobustnessGraph Neural Network	—Unverified
Enhance DNN Adversarial Robustness and Efficiency via Injecting Noise to Non-Essential Neurons	Feb 6, 2024	Adversarial RobustnessDecision Making	—Unverified
UFO-BLO: Unbiased First-Order Bilevel Optimization	Jun 5, 2020	Adversarial RobustnessBilevel Optimization	—Unverified
Boosting Certified Robustness for Time Series Classification with Efficient Self-Ensemble	Sep 4, 2024	Adversarial RobustnessTime Series	—Unverified
Semantics-Preserving Adversarial Training	Sep 23, 2020	Adversarial Robustness	—Unverified
Boosting Barely Robust Learners: A New Perspective on Adversarial Robustness	Feb 11, 2022	Adversarial Robustness	—Unverified
Enhancing Adversarial Robustness in SNNs with Sparse Gradients	May 30, 2024	Adversarial Robustness	—Unverified
Enhancing Adversarial Robustness of Deep Neural Networks Through Supervised Contrastive Learning	Dec 27, 2024	Adversarial RobustnessContrastive Learning	—Unverified
Enhancing Adversarial Robustness of Vision Language Models via Adversarial Mixture Prompt Tuning	May 23, 2025	Adversarial Robustness	—Unverified
Enhancing Adversarial Robustness via Uncertainty-Aware Distributional Adversarial Training	Nov 5, 2024	Adversarial RobustnessDiversity	—Unverified
Boosting Adversarial Robustness From The Perspective of Effective Margin Regularization	Oct 11, 2022	Adversarial DefenseAdversarial Robustness	—Unverified
Boosting Adversarial Robustness and Generalization with Structural Prior	Feb 2, 2025	Adversarial RobustnessDeep Learning	—Unverified
Semi-Implicit Hybrid Gradient Methods with Application to Adversarial Robustness	Feb 21, 2022	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