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 776–800 of 1746 papers

Title	Date	Tasks	Status
Projected Randomized Smoothing for Certified Adversarial Robustness	Sep 25, 2023	Adversarial Robustness	CodeCode Available
Improving Robustness of Deep Convolutional Neural Networks via Multiresolution Learning	Sep 24, 2023	Adversarial Robustness	—Unverified
RBFormer: Improve Adversarial Robustness of Transformer by Robust Bias	Sep 23, 2023	Adversarial Robustness	—Unverified
VIC-KD: Variance-Invariance-Covariance Knowledge Distillation to Make Keyword Spotting More Robust Against Adversarial Attacks	Sep 22, 2023	Adversarial RobustnessKeyword Spotting	—Unverified
On the Relationship between Skill Neurons and Robustness in Prompt Tuning	Sep 21, 2023	Adversarial Robustness	CodeCode Available
Language Guided Adversarial Purification	Sep 19, 2023	Adversarial DefenseAdversarial Purification	CodeCode Available
Evaluating Adversarial Robustness with Expected Viable Performance	Sep 18, 2023	Adversarial Robustness	—Unverified
DAD++: Improved Data-free Test Time Adversarial Defense	Sep 10, 2023	Adversarial DefenseAdversarial Robustness	CodeCode Available
Exploring Robust Features for Improving Adversarial Robustness	Sep 9, 2023	Adversarial RobustnessDisentanglement	—Unverified
Regret-Optimal Federated Transfer Learning for Kernel Regression with Applications in American Option Pricing	Sep 8, 2023	Adversarial Robustnessregression	CodeCode Available
Adversarially Robust Learning with Optimal Transport Regularized Divergences	Sep 7, 2023	Adversarial RobustnessDeep Learning	CodeCode Available
J-Guard: Journalism Guided Adversarially Robust Detection of AI-generated News	Sep 6, 2023	Adversarial RobustnessArticles	CodeCode Available
RobustEdge: Low Power Adversarial Detection for Cloud-Edge Systems	Sep 5, 2023	Adversarial RobustnessQuantization	—Unverified
Advancing Adversarial Robustness Through Adversarial Logit Update	Aug 29, 2023	Adversarial PurificationAdversarial Robustness	—Unverified
Prediction without Preclusion: Recourse Verification with Reachable Sets	Aug 24, 2023	Adversarial RobustnessPrediction	CodeCode Available
Fast Adversarial Training with Smooth Convergence	Aug 24, 2023	Adversarial Robustness	CodeCode Available
Don't Look into the Sun: Adversarial Solarization Attacks on Image Classifiers	Aug 24, 2023	Adversarial Robustnessimage-classification	CodeCode Available
Measuring the Effect of Causal Disentanglement on the Adversarial Robustness of Neural Network Models	Aug 21, 2023	Adversarial RobustnessBenchmarking	—Unverified
Causal Adversarial Perturbations for Individual Fairness and Robustness in Heterogeneous Data Spaces	Aug 17, 2023	Adversarial RobustnessFairness	—Unverified
Benchmarking Adversarial Robustness of Compressed Deep Learning Models	Aug 16, 2023	Adversarial RobustnessBenchmarking	—Unverified
Expressivity of Graph Neural Networks Through the Lens of Adversarial Robustness	Aug 16, 2023	Adversarial RobustnessSubgraph Counting	CodeCode Available
Enhancing the Antidote: Improved Pointwise Certifications against Poisoning Attacks	Aug 15, 2023	Adversarial Robustness	—Unverified
On the Interplay of Convolutional Padding and Adversarial Robustness	Aug 12, 2023	Adversarial Robustness	CodeCode Available
ModSec-AdvLearn: Countering Adversarial SQL Injections with Robust Machine Learning	Aug 9, 2023	Adversarial Robustness	CodeCode Available
Improving Performance of Semi-Supervised Learning by Adversarial Attacks	Aug 8, 2023	Adversarial Robustnessimage-classification	—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