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 76–100 of 1746 papers

Title	Date	Tasks	Status	Hype
Adversarial Visual Robustness by Causal Intervention	Jun 17, 2021	Adversarial Robustness	CodeCode Available	1
Adversarial Vulnerability of Randomized Ensembles	Jun 14, 2022	Adversarial AttackAdversarial Robustness	CodeCode Available	1
AdvRush: Searching for Adversarially Robust Neural Architectures	Aug 3, 2021	Adversarial RobustnessNeural Architecture Search	CodeCode Available	1
On the Adversarial Robustness of Vision Transformers	Mar 29, 2021	Adversarial Robustness	CodeCode Available	1
Adversarial Image Color Transformations in Explicit Color Filter Space	Nov 12, 2020	Adversarial Robustness	CodeCode Available	1
DRSM: De-Randomized Smoothing on Malware Classifier Providing Certified Robustness	Mar 20, 2023	Adversarial RobustnessMalware Detection	CodeCode Available	1
A Perturbation-Constrained Adversarial Attack for Evaluating the Robustness of Optical Flow	Mar 24, 2022	Adversarial AttackAdversarial Robustness	CodeCode Available	1
Adversarial Robustness in Graph Neural Networks: A Hamiltonian Approach	Oct 10, 2023	Adversarial Robustness	CodeCode Available	1
A Regularization Method to Improve Adversarial Robustness of Neural Networks for ECG Signal Classification	Oct 19, 2021	Adversarial RobustnessClassification	CodeCode Available	1
Adversarial Pruning: A Survey and Benchmark of Pruning Methods for Adversarial Robustness	Sep 2, 2024	Adversarial RobustnessNetwork Pruning	CodeCode Available	1
Adversarial Prompt Tuning for Vision-Language Models	Nov 19, 2023	Adversarial Robustness	CodeCode Available	1
Adversarial Robustness Limits via Scaling-Law and Human-Alignment Studies	Apr 14, 2024	Adversarial Robustness	CodeCode Available	1
Guardians of Image Quality: Benchmarking Defenses Against Adversarial Attacks on Image Quality Metrics	Aug 2, 2024	Adversarial AttackAdversarial Purification	CodeCode Available	1
Attacks Which Do Not Kill Training Make Adversarial Learning Stronger	Feb 26, 2020	Adversarial Robustness	CodeCode Available	1
Adversarial Robustness of Representation Learning for Knowledge Graphs	Sep 30, 2022	Adversarial RobustnessData Poisoning	CodeCode Available	1
Adversarially-Aware Robust Object Detector	Jul 13, 2022	Adversarial RobustnessObject	CodeCode Available	1
Adversarial Robustification via Text-to-Image Diffusion Models	Jul 26, 2024	Adversarial Robustnesszero-shot-classification	CodeCode Available	1
Adversarial Attacks on ML Defense Models Competition	Oct 15, 2021	Adversarial AttackAdversarial Robustness	CodeCode Available	1
BadPart: Unified Black-box Adversarial Patch Attacks against Pixel-wise Regression Tasks	Apr 1, 2024	Adversarial RobustnessAutonomous Driving	CodeCode Available	1
Adversarially Robust Distillation	May 23, 2019	Adversarial RobustnessKnowledge Distillation	CodeCode Available	1
Adversarial Robustness Against the Union of Multiple Perturbation Models	Sep 9, 2019	Adversarial Robustness	CodeCode Available	1
Adversarial Robustness Comparison of Vision Transformer and MLP-Mixer to CNNs	Oct 6, 2021	Adversarial AttackAdversarial Robustness	CodeCode Available	1
Bridging Mode Connectivity in Loss Landscapes and Adversarial Robustness	Apr 30, 2020	Adversarial Robustness	CodeCode Available	1
CARBEN: Composite Adversarial Robustness Benchmark	Jul 16, 2022	Adversarial AttackAdversarial Robustness	CodeCode Available	1
Adversarial Robustness against Multiple and Single l_p-Threat Models via Quick Fine-Tuning of Robust Classifiers	May 26, 2021	Adversarial Robustness	CodeCode Available	1

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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