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Adversarial Attack

An Adversarial Attack is a technique to find a perturbation that changes the prediction of a machine learning model. The perturbation can be very small and imperceptible to human eyes.

Source: Recurrent Attention Model with Log-Polar Mapping is Robust against Adversarial Attacks

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 1341–1350 of 1808 papers

Title	Date	Tasks	Status	Hype	Score
Replace-then-Perturb: Targeted Adversarial Attacks With Visual Reasoning for Vision-Language Models	Nov 1, 2024	Adversarial AttackContrastive Learning	—Unverified	0	0
Residue-Based Natural Language Adversarial Attack Detection	Jan 16, 2022	Adversarial AttackAdversarial Attack Detection	—Unverified	0	0
Transferable Adversarial Attack for Both Vision Transformers and Convolutional Networks via Momentum Integrated Gradients	Jan 1, 2023	Adversarial Attack	—Unverified	0	0
Transferable Adversarial Attack on Image Tampering Localization	Sep 19, 2023	Adversarial Attack	—Unverified	0	0
Resilient and constrained consensus against adversarial attacks: A distributed MPC framework	Nov 10, 2023	Adversarial AttackAdversarial Attack Detection	—Unverified	0	0
Resilient Dynamic Average Consensus based on Trusted agents	Mar 14, 2023	Adversarial Attack	—Unverified	0	0
Resilient Learning-Based Control for Synchronization of Passive Multi-Agent Systems under Attack	Sep 28, 2017	Adversarial Attack	—Unverified	0	0
Adaptive Adversarial Training Does Not Increase Recourse Costs	Sep 5, 2023	Adversarial Attack	—Unverified	0	0
Resisting Graph Adversarial Attack via Cooperative Homophilous Augmentation	Nov 15, 2022	Adversarial Attack	—Unverified	0	0
Restricted Black-box Adversarial Attack Against DeepFake Face Swapping	Apr 26, 2022	Adversarial AttackFace Reconstruction	—Unverified	0	0

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All datasets CIFAR-10 CIFAR-100

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	Xu et al.	Attack: PGD20	78.68	—	Unverified
2	3-ensemble of multi-resolution self-ensembles	Attack: AutoAttack	78.13	—	Unverified
3	TRADES-ANCRA/ResNet18	Attack: AutoAttack	59.7	—	Unverified
4	AdvTraining [madry2018]	Attack: PGD20	48.44	—	Unverified
5	TRADES [zhang2019b]	Attack: PGD20	45.9	—	Unverified
6	XU-Net	Robust Accuracy	1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	3-ensemble of multi-resolution self-ensembles	Attack: AutoAttack	51.28	—	Unverified
2	multi-resolution self-ensembles	Attack: AutoAttack	47.85	—	Unverified