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
Simple Post-Training Robustness Using Test Time Augmentations and Random Forest	Sep 16, 2021	Adversarial RobustnessDiversity	CodeCode Available
Adversarial Examples for Evaluating Math Word Problem Solvers	Sep 13, 2021	Adversarial RobustnessMath	CodeCode Available
Adversarial Bone Length Attack on Action Recognition	Sep 13, 2021	Action RecognitionAdversarial Robustness	—Unverified
2-in-1 Accelerator: Enabling Random Precision Switch for Winning Both Adversarial Robustness and Efficiency	Sep 11, 2021	Adversarial RobustnessQuantization	—Unverified
Utilizing Adversarial Targeted Attacks to Boost Adversarial Robustness	Sep 4, 2021	Adversarial AttackAdversarial Robustness	—Unverified
Adversarial Robustness for Unsupervised Domain Adaptation	Sep 2, 2021	Adversarial RobustnessDomain Adaptation	—Unverified
Impact of Attention on Adversarial Robustness of Image Classification Models	Sep 2, 2021	Adversarial RobustnessClassification	—Unverified
Sample Efficient Detection and Classification of Adversarial Attacks via Self-Supervised Embeddings	Aug 30, 2021	Adversarial Robustness	—Unverified
A Hierarchical Assessment of Adversarial Severity	Aug 26, 2021	Adversarial Robustness	CodeCode Available
Understanding the Logit Distributions of Adversarially-Trained Deep Neural Networks	Aug 26, 2021	Adversarial Robustness	—Unverified
Adversarially Robust One-class Novelty Detection	Aug 25, 2021	Adversarial RobustnessNovelty Detection	CodeCode Available
Bridged Adversarial Training	Aug 25, 2021	Adversarial Robustness	—Unverified
Adversarial Robustness of Deep Learning: Theory, Algorithms, and Applications	Aug 24, 2021	Adversarial RobustnessDeep Learning	—Unverified
SegMix: Co-occurrence Driven Mixup for Semantic Segmentation and Adversarial Robustness	Aug 23, 2021	Adversarial RobustnessDenoising	—Unverified
ASAT: Adaptively Scaled Adversarial Training in Time Series	Aug 20, 2021	Adversarial RobustnessTime Series	—Unverified
Pruning in the Face of Adversaries	Aug 19, 2021	Adversarial RobustnessNetwork Pruning	CodeCode Available
STAR: Noisy Semi-Supervised Transfer Learning for Visual Classification	Aug 18, 2021	Adversarial RobustnessClassification	—Unverified
Neural Architecture Dilation for Adversarial Robustness	Aug 16, 2021	Adversarial Robustness	—Unverified
On the Effect of Pruning on Adversarial Robustness	Aug 10, 2021	Adversarial Robustness	—Unverified
Robust Transfer Learning with Pretrained Language Models through Adapters	Aug 5, 2021	Adversarial AttackAdversarial Robustness	—Unverified
Towards Adversarially Robust and Domain Generalizable Stereo Matching by Rethinking DNN Feature Backbones	Jul 31, 2021	Adversarial RobustnessStereo Matching	—Unverified
Who's Afraid of Thomas Bayes?	Jul 30, 2021	Adversarial Robustness	—Unverified
Robust Explainability: A Tutorial on Gradient-Based Attribution Methods for Deep Neural Networks	Jul 23, 2021	Adversarial Robustness	—Unverified
AdvFilter: Predictive Perturbation-aware Filtering against Adversarial Attack via Multi-domain Learning	Jul 14, 2021	Adversarial AttackAdversarial Robustness	—Unverified
Perceptual-based deep-learning denoiser as a defense against adversarial attacks on ASR systems	Jul 12, 2021	Adversarial RobustnessAutomatic Speech Recognition	—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