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 1151–1175 of 1746 papers

Title	Date	Tasks	Status
Holistic Adversarial Robustness of Deep Learning Models	Feb 15, 2022	Adversarial RobustnessDeep Learning	—Unverified
Unlabeled Data Help: Minimax Analysis and Adversarial Robustness	Feb 14, 2022	Adversarial RobustnessSelf-Supervised Learning	—Unverified
Finding Dynamics Preserving Adversarial Winning Tickets	Feb 14, 2022	Adversarial Robustness	—Unverified
Boosting Barely Robust Learners: A New Perspective on Adversarial Robustness	Feb 11, 2022	Adversarial Robustness	—Unverified
D4: Detection of Adversarial Diffusion Deepfakes Using Disjoint Ensembles	Feb 11, 2022	Adversarial RobustnessDeepFake Detection	—Unverified
Deadwooding: Robust Global Pruning for Deep Neural Networks	Feb 10, 2022	Adversarial Robustness	—Unverified
On The Empirical Effectiveness of Unrealistic Adversarial Hardening Against Realistic Adversarial Attacks	Feb 7, 2022	Adversarial RobustnessMalware Detection	CodeCode Available
Optimized Potential Initialization for Low-latency Spiking Neural Networks	Feb 3, 2022	Adversarial Robustness	—Unverified
Finding Biological Plausibility for Adversarially Robust Features via Metameric Tasks	Feb 2, 2022	Adversarial RobustnessTexture Synthesis	CodeCode Available
Smoothed Embeddings for Certified Few-Shot Learning	Feb 2, 2022	Adversarial RobustnessFew-Shot Learning	CodeCode Available
Adversarial Robustness in Deep Learning: Attacks on Fragile Neurons	Jan 31, 2022	Adversarial AttackAdversarial Robustness	—Unverified
Improving Robustness by Enhancing Weak Subnets	Jan 30, 2022	Adversarial RobustnessData Augmentation	CodeCode Available
The Many Faces of Adversarial Risk	Jan 22, 2022	Adversarial Robustness	—Unverified
Unveiling Project-Specific Bias in Neural Code Models	Jan 19, 2022	Adversarial RobustnessVulnerability Detection	—Unverified
Tools and Practices for Responsible AI Engineering	Jan 14, 2022	Adversarial Robustness	—Unverified
Towards Adversarially Robust Deep Image Denoising	Jan 12, 2022	Adversarial AttackAdversarial Robustness	—Unverified
GenLabel: Mixup Relabeling using Generative Models	Jan 7, 2022	Adversarial RobustnessData Augmentation	—Unverified
Rethinking Feature Uncertainty in Stochastic Neural Networks for Adversarial Robustness	Jan 1, 2022	Adversarial Robustness	—Unverified
Improving the Behaviour of Vision Transformers with Token-consistent Stochastic Layers	Dec 30, 2021	Adversarial RobustnessTransfer Learning	—Unverified
Associative Adversarial Learning Based on Selective Attack	Dec 28, 2021	Adversarial RobustnessFew-Shot Learning	—Unverified
Perlin Noise Improve Adversarial Robustness	Dec 26, 2021	Adversarial Robustness	—Unverified
Understanding and Measuring Robustness of Multimodal Learning	Dec 22, 2021	Adversarial Robustness	—Unverified
Improving Robustness with Image Filtering	Dec 21, 2021	Adversarial RobustnessData Augmentation	—Unverified
On the Adversarial Robustness of Causal Algorithmic Recourse	Dec 21, 2021	Adversarial RobustnessDecision Making	CodeCode Available
Certified Federated Adversarial Training	Dec 20, 2021	Adversarial RobustnessFederated Learning	—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