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 1601–1625 of 1746 papers

Title	Date	Tasks	Status
LOT: Layer-wise Orthogonal Training on Improving _2 Certified Robustness	Oct 20, 2022	Adversarial Robustness	CodeCode Available
Adaptive Meta-Learning for Robust Deepfake Detection: A Multi-Agent Framework to Data Drift and Model Generalization	Nov 12, 2024	Adversarial RobustnessDeepFake Detection	CodeCode Available
Lower Bounds on Adversarial Robustness from Optimal Transport	Sep 26, 2019	Adversarial RobustnessClassification	CodeCode Available
TrackPGD: Efficient Adversarial Attack using Object Binary Masks against Robust Transformer Trackers	Jul 4, 2024	Adversarial AttackAdversarial Robustness	CodeCode Available
Revisiting the Adversarial Robustness of Vision Language Models: a Multimodal Perspective	Apr 30, 2024	Adversarial DefenseAdversarial Robustness	CodeCode Available
LRS: Enhancing Adversarial Transferability through Lipschitz Regularized Surrogate	Dec 20, 2023	Adversarial Robustness	CodeCode Available
Debona: Decoupled Boundary Network Analysis for Tighter Bounds and Faster Adversarial Robustness Proofs	Jun 16, 2020	Adversarial Robustness	CodeCode Available
A Curious Case of Searching for the Correlation between Training Data and Adversarial Robustness of Transformer Textual Models	Feb 18, 2024	Adversarial RobustnessAdversarial Text	CodeCode Available
Revisiting the Trade-off between Accuracy and Robustness via Weight Distribution of Filters	Jun 6, 2023	Adversarial Robustness	CodeCode Available
RoAST: Robustifying Language Models via Adversarial Perturbation with Selective Training	Dec 7, 2023	Adversarial Robustness	CodeCode Available
Robust and Accurate Object Detection via Self-Knowledge Distillation	Nov 14, 2021	Adversarial RobustnessKnowledge Distillation	CodeCode Available
DD-RobustBench: An Adversarial Robustness Benchmark for Dataset Distillation	Mar 20, 2024	Adversarial AttackAdversarial Robustness	CodeCode Available
Increasing the adversarial robustness and explainability of capsule networks with γ-capsules	Dec 23, 2018	Adversarial RobustnessInductive Bias	CodeCode Available
MMA Training: Direct Input Space Margin Maximization through Adversarial Training	Dec 6, 2018	Adversarial DefenseAdversarial Robustness	CodeCode Available
The King is Naked: on the Notion of Robustness for Natural Language Processing	Dec 13, 2021	Adversarial Robustness	CodeCode Available
Are Generative Classifiers More Robust to Adversarial Attacks?	Feb 19, 2018	Adversarial DefenseAdversarial Robustness	CodeCode Available
Architectural Resilience to Foreground-and-Background Adversarial Noise	Mar 23, 2020	Adversarial Robustness	CodeCode Available
ME-Net: Towards Effective Adversarial Robustness with Matrix Estimation	May 28, 2019	Adversarial Robustness	CodeCode Available
DAT: Improving Adversarial Robustness via Generative Amplitude Mix-up in Frequency Domain	Oct 16, 2024	Adversarial Robustness	CodeCode Available
Adversarially Robust Learning with Optimal Transport Regularized Divergences	Sep 7, 2023	Adversarial RobustnessDeep Learning	CodeCode Available
Metric Learning for Adversarial Robustness	Sep 3, 2019	Adversarial RobustnessMetric Learning	CodeCode Available
Metrics and methods for robustness evaluation of neural networks with generative models	Mar 4, 2020	Adversarial Robustnessimage-classification	CodeCode Available
Data Quality Matters For Adversarial Training: An Empirical Study	Feb 15, 2021	Adversarial Robustness	CodeCode Available
MIMIR: Masked Image Modeling for Mutual Information-based Adversarial Robustness	Dec 8, 2023	Adversarial Robustness	CodeCode Available
Training for Faster Adversarial Robustness Verification via Inducing ReLU Stability	Sep 9, 2018	Adversarial Robustness	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