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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 1401–1410 of 1808 papers

Title	Date	Tasks	Status	Hype	Score
Adversarial attacks on deep learning models for fatty liver disease classification by modification of ultrasound image reconstruction method	Sep 7, 2020	Adversarial AttackImage Reconstruction	—Unverified	0	0
Robustness of Selected Learning Models under Label-Flipping Attack	Jan 21, 2025	Adversarial Attack	—Unverified	0	0
Robust Optimal Power Flow Against Adversarial Attacks: A Tri-Level Optimization Approach	Nov 13, 2024	Adversarial Attack	—Unverified	0	0
Tropical Attention: Neural Algorithmic Reasoning for Combinatorial Algorithms	May 22, 2025	Adversarial AttackBenchmarking	—Unverified	0	0
Robust Physical-World Attacks on Face Recognition	Sep 20, 2021	Adversarial AttackAdversarial Robustness	—Unverified	0	0
RoVISQ: Reduction of Video Service Quality via Adversarial Attacks on Deep Learning-based Video Compression	Mar 18, 2022	Adversarial AttackClassification	—Unverified	0	0
When Side-Channel Attacks Break the Black-Box Property of Embedded Artificial Intelligence	Nov 23, 2023	Adversarial Attackobject-detection	—Unverified	0	0
Trustworthy Actionable Perturbations	May 18, 2024	Adversarial Attackcounterfactual	—Unverified	0	0
A Context-Aware Approach for Textual Adversarial Attack through Probability Difference Guided Beam Search	Aug 17, 2022	Adversarial Attack	—Unverified	0	0
Robust saliency maps with distribution-preserving decoys	Sep 25, 2019	Adversarial Attack	—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