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
Multi-scale Diffusion Denoised Smoothing	Oct 25, 2023	Adversarial RobustnessDenoising	CodeCode Available	1
OODRobustBench: a Benchmark and Large-Scale Analysis of Adversarial Robustness under Distribution Shift	Oct 19, 2023	Adversarial RobustnessBenchmarking	CodeCode Available	1
IRAD: Implicit Representation-driven Image Resampling against Adversarial Attacks	Oct 18, 2023	Adversarial Robustness	CodeCode Available	1
To Generate or Not? Safety-Driven Unlearned Diffusion Models Are Still Easy To Generate Unsafe Images ... For Now	Oct 18, 2023	Adversarial Robustness	CodeCode Available	1
Improving Fast Minimum-Norm Attacks with Hyperparameter Optimization	Oct 12, 2023	Adversarial RobustnessHyperparameter Optimization	CodeCode Available	1
Comparing the Robustness of Modern No-Reference Image- and Video-Quality Metrics to Adversarial Attacks	Oct 10, 2023	Adversarial Robustness	CodeCode Available	1
Adversarial Robustness in Graph Neural Networks: A Hamiltonian Approach	Oct 10, 2023	Adversarial Robustness	CodeCode Available	1
VLATTACK: Multimodal Adversarial Attacks on Vision-Language Tasks via Pre-trained Models	Oct 7, 2023	Adversarial Robustness	CodeCode Available	1
How Robust is Google's Bard to Adversarial Image Attacks?	Sep 21, 2023	Adversarial RobustnessChatbot	CodeCode Available	1
Robust Principles: Architectural Design Principles for Adversarially Robust CNNs	Aug 30, 2023	Adversarial Robustness	CodeCode Available	1
Revisiting and Exploring Efficient Fast Adversarial Training via LAW: Lipschitz Regularization and Auto Weight Averaging	Aug 22, 2023	Adversarial RobustnessData Augmentation	CodeCode Available	1
On the Adversarial Robustness of Multi-Modal Foundation Models	Aug 21, 2023	Adversarial AttackAdversarial Robustness	CodeCode Available	1
HoSNN: Adversarially-Robust Homeostatic Spiking Neural Networks with Adaptive Firing Thresholds	Aug 20, 2023	Adversarial Robustness	CodeCode Available	1
Improving Adversarial Robustness of Masked Autoencoders via Test-time Frequency-domain Prompting	Aug 20, 2023	Adversarial Robustness	CodeCode Available	1
Robust Mixture-of-Expert Training for Convolutional Neural Networks	Aug 19, 2023	Adversarial Robustness	CodeCode Available	1
TrajPAC: Towards Robustness Verification of Pedestrian Trajectory Prediction Models	Aug 11, 2023	Adversarial RobustnessAutonomous Vehicles	CodeCode Available	1
Large Language Models to Identify Social Determinants of Health in Electronic Health Records	Aug 11, 2023	Adversarial RobustnessEthics	CodeCode Available	1
Benchmarking and Analyzing Robust Point Cloud Recognition: Bag of Tricks for Defending Adversarial Examples	Jul 31, 2023	Adversarial RobustnessBenchmarking	CodeCode Available	1
HybridAugment++: Unified Frequency Spectra Perturbations for Model Robustness	Jul 21, 2023	Adversarial RobustnessData Augmentation	CodeCode Available	1
Mitigating Adversarial Vulnerability through Causal Parameter Estimation by Adversarial Double Machine Learning	Jul 14, 2023	Adversarial Robustnessparameter estimation	CodeCode Available	1
Enhancing Adversarial Robustness via Score-Based Optimization	Jul 10, 2023	Adversarial DefenseAdversarial Robustness	CodeCode Available	1
Mitigating Accuracy-Robustness Trade-off via Balanced Multi-Teacher Adversarial Distillation	Jun 28, 2023	Adversarial RobustnessKnowledge Distillation	CodeCode Available	1
Robust Spatiotemporal Traffic Forecasting with Reinforced Dynamic Adversarial Training	Jun 25, 2023	Adversarial RobustnessKnowledge Distillation	CodeCode Available	1
Similarity Preserving Adversarial Graph Contrastive Learning	Jun 24, 2023	Adversarial RobustnessContrastive Learning	CodeCode Available	1
Towards Reliable Evaluation and Fast Training of Robust Semantic Segmentation Models	Jun 22, 2023	Adversarial Robustnessimage-classification	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