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 551–575 of 1746 papers

Title	Date	Tasks	Status
Benchmarking Adversarial Robustness of Compressed Deep Learning Models	Aug 16, 2023	Adversarial RobustnessBenchmarking	—Unverified
Benchmarking Adversarial Robustness	Dec 26, 2019	Adversarial AttackAdversarial Robustness	—Unverified
Adversarial Robustness of Similarity-Based Link Prediction	Sep 3, 2019	Adversarial RobustnessLink Prediction	—Unverified
Exploring Robust Features for Improving Adversarial Robustness	Sep 9, 2023	Adversarial RobustnessDisentanglement	—Unverified
Exploring the Adversarial Frontier: Quantifying Robustness via Adversarial Hypervolume	Mar 8, 2024	Adversarial RobustnessBenchmarking	—Unverified
Adversarial Robustness of Program Synthesis Models	Oct 8, 2021	Adversarial RobustnessProgram Synthesis	—Unverified
CE-based white-box adversarial attacks will not work using super-fitting	May 4, 2022	Adversarial AttackAdversarial Robustness	—Unverified
Adversarial Information Bottleneck	Feb 28, 2021	Adversarial Robustness	—Unverified
BARReL: Bottleneck Attention for Adversarial Robustness in Vision-Based Reinforcement Learning	Aug 22, 2022	Adversarial Robustnessreinforcement-learning	—Unverified
A case for new neural networks smoothness constraints	Oct 19, 2020	Adversarial RobustnessInductive Bias	—Unverified
Exploring the Adversarial Robustness of CLIP for AI-generated Image Detection	Jul 28, 2024	Adversarial Robustness	—Unverified
Exponential Separation between Two Learning Models and Adversarial Robustness	Dec 1, 2021	Adversarial RobustnessPAC learning	—Unverified
Fair Robust Active Learning by Joint Inconsistency	Sep 22, 2022	Active LearningAdversarial Attack	—Unverified
Backdoor Attacks Against Incremental Learners: An Empirical Evaluation Study	May 28, 2023	Adversarial RobustnessBackdoor Attack	—Unverified
Adversarial Robustness of Probabilistic Network Embedding for Link Prediction	Jul 5, 2021	Adversarial RobustnessLink Prediction	—Unverified
aw_nas: A Modularized and Extensible NAS framework	Nov 25, 2020	Adversarial RobustnessNeural Architecture Search	—Unverified
Average Margin Regularization for Classifiers	Oct 9, 2018	Adversarial Robustness	—Unverified
Adversarial Robustness of Partitioned Quantum Classifiers	Jan 28, 2025	Adversarial RobustnessQuantum Machine Learning	—Unverified
Exploring Adversarial Robustness of LiDAR-Camera Fusion Model in Autonomous Driving	Dec 3, 2023	3D Object DetectionAdversarial Robustness	—Unverified
Exploring adversarial robustness of JPEG AI: methodology, comparison and new methods	Nov 18, 2024	Adversarial RobustnessImage Compression	—Unverified
Exploring Adversarial Robustness of Multi-Sensor Perception Systems in Self Driving	Jan 17, 2021	Adversarial RobustnessDenoising	—Unverified
AutoLoRa: A Parameter-Free Automated Robust Fine-Tuning Framework	Oct 3, 2023	Adversarial RobustnessScheduling	—Unverified
Adversarial Fine-tune with Dynamically Regulated Adversary	Apr 28, 2022	Adversarial AttackAdversarial Robustness	—Unverified
Exploring Adversarial Transferability between Kolmogorov-arnold Networks	Mar 8, 2025	Adversarial RobustnessKolmogorov-Arnold Networks	—Unverified
A Useful Taxonomy for Adversarial Robustness of Neural Networks	Oct 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