SOTAVerified|Agents Browse Leaderboard About Blog

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 1551–1560 of 1808 papers

Title	Date	Tasks	Status	Hype
AICAttack: Adversarial Image Captioning Attack with Attention-Based Optimization	Feb 19, 2024	Adversarial AttackImage Captioning	CodeCode Available	0
Improving Transferability of Adversarial Examples with Input Diversity	Mar 19, 2018	Adversarial AttackDiversity	CodeCode Available	0
Hidden Activations Are Not Enough: A General Approach to Neural Network Predictions	Sep 20, 2024	Adversarial Attack	CodeCode Available	0
Query-efficient Meta Attack to Deep Neural Networks	Jun 6, 2019	Adversarial AttackMeta-Learning	CodeCode Available	0
A Hierarchical Feature Constraint to Camouflage Medical Adversarial Attacks	Dec 17, 2020	Adversarial AttackDecision Making	CodeCode Available	0
A Game-Based Approximate Verification of Deep Neural Networks with Provable Guarantees	Jul 10, 2018	Adversarial AttackAdversarial Defense	CodeCode Available	0
A Frank-Wolfe Framework for Efficient and Effective Adversarial Attacks	Nov 27, 2018	Adversarial Attack	CodeCode Available	0
Sparse and Imperceptible Adversarial Attack via a Homotopy Algorithm	Jun 10, 2021	Adversarial Attack	CodeCode Available	0
Enhancing Adversarial Attacks: The Similar Target Method	Aug 21, 2023	Adversarial Attack	CodeCode Available	0
Injecting and removing malignant features in mammography with CycleGAN: Investigation of an automated adversarial attack using neural networks	Nov 19, 2018	Adversarial AttackGenerative Adversarial Network	CodeCode Available	0

Show:10 25 50

← PrevPage 156 of 181Next →

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