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	Hype
Adversarial Robustness of Deep Learning: Theory, Algorithms, and Applications	Aug 24, 2021	Adversarial RobustnessDeep Learning	—Unverified	0
SegMix: Co-occurrence Driven Mixup for Semantic Segmentation and Adversarial Robustness	Aug 23, 2021	Adversarial RobustnessDenoising	—Unverified	0
AdvDrop: Adversarial Attack to DNNs by Dropping Information	Aug 20, 2021	Adversarial AttackAdversarial Robustness	CodeCode Available	1
ASAT: Adaptively Scaled Adversarial Training in Time Series	Aug 20, 2021	Adversarial RobustnessTime Series	—Unverified	0
Pruning in the Face of Adversaries	Aug 19, 2021	Adversarial RobustnessNetwork Pruning	CodeCode Available	0
STAR: Noisy Semi-Supervised Transfer Learning for Visual Classification	Aug 18, 2021	Adversarial RobustnessClassification	—Unverified	0
Revisiting Adversarial Robustness Distillation: Robust Soft Labels Make Student Better	Aug 18, 2021	Adversarial RobustnessKnowledge Distillation	CodeCode Available	1
Neural Architecture Dilation for Adversarial Robustness	Aug 16, 2021	Adversarial Robustness	—Unverified	0
AGKD-BML: Defense Against Adversarial Attack by Attention Guided Knowledge Distillation and Bi-directional Metric Learning	Aug 13, 2021	Adversarial AttackAdversarial Robustness	CodeCode Available	1
On the Effect of Pruning on Adversarial Robustness	Aug 10, 2021	Adversarial Robustness	—Unverified	0
Improved deterministic l2 robustness on CIFAR-10 and CIFAR-100	Aug 5, 2021	Adversarial Robustness	CodeCode Available	1
Robust Transfer Learning with Pretrained Language Models through Adapters	Aug 5, 2021	Adversarial AttackAdversarial Robustness	—Unverified	0
AdvRush: Searching for Adversarially Robust Neural Architectures	Aug 3, 2021	Adversarial RobustnessNeural Architecture Search	CodeCode Available	1
Towards Adversarially Robust and Domain Generalizable Stereo Matching by Rethinking DNN Feature Backbones	Jul 31, 2021	Adversarial RobustnessStereo Matching	—Unverified	0
Who's Afraid of Thomas Bayes?	Jul 30, 2021	Adversarial Robustness	—Unverified	0
Enhancing Adversarial Robustness via Test-time Transformation Ensembling	Jul 29, 2021	Adversarial Robustness	CodeCode Available	1
WaveCNet: Wavelet Integrated CNNs to Suppress Aliasing Effect for Noise-Robust Image Classification	Jul 28, 2021	Adversarial Robustnessimage-classification	CodeCode Available	1
Clipped Hyperbolic Classifiers Are Super-Hyperbolic Classifiers	Jul 23, 2021	Adversarial RobustnessGeneral Classification	CodeCode Available	1
Robust Explainability: A Tutorial on Gradient-Based Attribution Methods for Deep Neural Networks	Jul 23, 2021	Adversarial Robustness	—Unverified	0
Fast and Scalable Adversarial Training of Kernel SVM via Doubly Stochastic Gradients	Jul 21, 2021	Adversarial Robustness	CodeCode Available	1
AdvFilter: Predictive Perturbation-aware Filtering against Adversarial Attack via Multi-domain Learning	Jul 14, 2021	Adversarial AttackAdversarial Robustness	—Unverified	0
A Closer Look at the Adversarial Robustness of Information Bottleneck Models	Jul 12, 2021	Adversarial Robustness	—Unverified	0
Perceptual-based deep-learning denoiser as a defense against adversarial attacks on ASR systems	Jul 12, 2021	Adversarial RobustnessAutomatic Speech Recognition	—Unverified	0
Improving Model Robustness with Latent Distribution Locally and Globally	Jul 8, 2021	Adversarial Robustness	CodeCode Available	0
Understanding Intrinsic Robustness Using Label Uncertainty	Jul 7, 2021	Adversarial RobustnessClassification	CodeCode Available	0

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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