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
Adversarial Robustness of Similarity-Based Link Prediction	Sep 3, 2019	Adversarial RobustnessLink Prediction	—Unverified
Toward Spiking Neural Network Local Learning Modules Resistant to Adversarial Attacks	Apr 11, 2025	Adversarial AttackAdversarial Robustness	—Unverified
Prototypical Examples in Deep Learning: Metrics, Characteristics, and Utility	May 1, 2019	Adversarial RobustnessDeep Learning	—Unverified
Pro-tuning: Unified Prompt Tuning for Vision Tasks	Jul 28, 2022	Adversarial Robustnessimage-classification	—Unverified
What are effective labels for augmented data? Improving robustness with AutoLabel	Jan 1, 2021	Adversarial RobustnessData Augmentation	—Unverified
Provable Adversarial Robustness for Group Equivariant Tasks: Graphs, Point Clouds, Molecules, and More	Dec 5, 2023	Adversarial RobustnessMolecular Property Prediction	—Unverified
Provable Defense Against Clustering Attacks on 3D Point Clouds	Nov 22, 2021	Adversarial RobustnessClustering	—Unverified
Provable Unrestricted Adversarial Training without Compromise with Generalizability	Jan 22, 2023	Adversarial Robustness	—Unverified
Adversarial Robustness of Program Synthesis Models	Oct 8, 2021	Adversarial RobustnessProgram Synthesis	—Unverified
Towards Proving the Adversarial Robustness of Deep Neural Networks	Sep 8, 2017	Adversarial RobustnessAutonomous Vehicles	—Unverified
Towards quantum enhanced adversarial robustness in machine learning	Jun 22, 2023	Adversarial RobustnessComputational Efficiency	—Unverified
Provably Robust Transfer	Sep 29, 2021	Adversarial RobustnessTransfer Learning	—Unverified
Adversarial Robustness of Probabilistic Network Embedding for Link Prediction	Jul 5, 2021	Adversarial RobustnessLink Prediction	—Unverified
Towards Resilient and Efficient LLMs: A Comparative Study of Efficiency, Performance, and Adversarial Robustness	Aug 8, 2024	Adversarial Robustness	—Unverified
Push-Pull: Characterizing the Adversarial Robustness for Audio-Visual Active Speaker Detection	Oct 3, 2022	Active Speaker DetectionAdversarial Robustness	—Unverified
Towards Robust and Accurate Stability Estimation of Local Surrogate Models in Text-based Explainable AI	Jan 3, 2025	Adversarial Robustness	—Unverified
Adversarial Robustness of Partitioned Quantum Classifiers	Jan 28, 2025	Adversarial RobustnessQuantum Machine Learning	—Unverified
Q-TART: Quickly Training for Adversarial Robustness and in-Transferability	Apr 14, 2022	Adversarial Robustness	—Unverified
QUANOS- Adversarial Noise Sensitivity Driven Hybrid Quantization of Neural Networks	Apr 22, 2020	Adversarial RobustnessQuantization	—Unverified
Quantifying Adversarial Sensitivity of a Model as a Function of the Image Distribution	Oct 9, 2020	Adversarial RobustnessObject Recognition	—Unverified
Adversarial Robustness of In-Context Learning in Transformers for Linear Regression	Nov 7, 2024	Adversarial RobustnessIn-Context Learning	—Unverified
Quantitative Analysis of Deeply Quantized Tiny Neural Networks Robust to Adversarial Attacks	Mar 12, 2025	Adversarial RobustnessQuantization	—Unverified
Towards Robust and Accurate Visual Prompting	Nov 18, 2023	Adversarial RobustnessTransfer Learning	—Unverified
Quantum Neural Networks under Depolarization Noise: Exploring White-Box Attacks and Defenses	Nov 29, 2023	Adversarial RobustnessMulti-class Classification	—Unverified
Quantum Support Vector Regression for Robust Anomaly Detection	May 2, 2025	Adversarial RobustnessAnomaly Detection	—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