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 1726–1746 of 1746 papers

Title	Date	Tasks	Status
Adversarial Feature Desensitization	Jun 8, 2020	Adversarial RobustnessDomain Adaptation	CodeCode Available
Towards Adversarially Robust Dataset Distillation by Curvature Regularization	Mar 15, 2024	Adversarial RobustnessDataset Distillation	CodeCode Available
Two Heads are Better than One: Robust Learning Meets Multi-branch Models	Aug 17, 2022	Adversarial RobustnessPhilosophy	CodeCode Available
On the Connection Between Adversarial Robustness and Saliency Map Interpretability	May 10, 2019	Adversarial Robustness	CodeCode Available
CARTL: Cooperative Adversarially-Robust Transfer Learning	Jun 12, 2021	Adversarial RobustnessTransfer Learning	CodeCode Available
Using Wavelets and Spectral Methods to Study Patterns in Image-Classification Datasets	Jun 17, 2020	Adversarial RobustnessGeneral Classification	CodeCode Available
Using Z3 for Formal Modeling and Verification of FNN Global Robustness	Apr 20, 2023	Adversarial Robustness	CodeCode Available
RoHNAS: A Neural Architecture Search Framework with Conjoint Optimization for Adversarial Robustness and Hardware Efficiency of Convolutional and Capsule Networks	Oct 11, 2022	Adversarial RobustnessGPU	CodeCode Available
Two Souls in an Adversarial Image: Towards Universal Adversarial Example Detection using Multi-view Inconsistency	Sep 25, 2021	Adversarial Attack DetectionAdversarial Defense	CodeCode Available
On The Empirical Effectiveness of Unrealistic Adversarial Hardening Against Realistic Adversarial Attacks	Feb 7, 2022	Adversarial RobustnessMalware Detection	CodeCode Available
Towards Adversarial Patch Analysis and Certified Defense against Crowd Counting	Apr 22, 2021	Adversarial AttackAdversarial Robustness	CodeCode Available
ROSE: Robust Selective Fine-tuning for Pre-trained Language Models	Oct 18, 2022	Adversarial Robustness	CodeCode Available
On the Sensitivity and Stability of Model Interpretations in NLP	Apr 18, 2021	Adversarial RobustnessDependency Parsing	CodeCode Available
RSC-SNN: Exploring the Trade-off Between Adversarial Robustness and Accuracy in Spiking Neural Networks via Randomized Smoothing Coding	Jul 29, 2024	Adversarial Robustness	CodeCode Available
On the human-recognizability phenomenon of adversarially trained deep image classifiers	Dec 18, 2020	Adversarial Robustness	CodeCode Available
On the Importance of Backbone to the Adversarial Robustness of Object Detectors	May 27, 2023	Adversarial RobustnessAutonomous Driving	CodeCode Available
Exploring Adversarial Robustness of Vision Transformers in the Spectral Perspective	Aug 20, 2022	Adversarial Robustnessimage-classification	CodeCode Available
On the Interplay of Convolutional Padding and Adversarial Robustness	Aug 12, 2023	Adversarial Robustness	CodeCode Available
On the Limitations of Stochastic Pre-processing Defenses	Jun 19, 2022	Adversarial Robustness	CodeCode Available
Two Tales of Single-Phase Contrastive Hebbian Learning	Feb 13, 2024	Adversarial Robustness	CodeCode Available
Adversarial Robustness by Design through Analog Computing and Synthetic Gradients	Jan 6, 2021	Adversarial RobustnessBinarization	CodeCode Available

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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