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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 1521–1530 of 1808 papers

Title	Date	Tasks	Status	Hype
An Adversarial Approach for Explaining the Predictions of Deep Neural Networks	May 20, 2020	Adversarial AttackBIG-bench Machine Learning	CodeCode Available	0
A Multi-task Adversarial Attack Against Face Authentication	Aug 15, 2024	Adversarial AttackManagement	CodeCode Available	0
In-distribution adversarial attacks on object recognition models using gradient-free search	Jun 30, 2021	Adversarial AttackAttribute	CodeCode Available	0
Efficient Formal Safety Analysis of Neural Networks	Sep 19, 2018	Adversarial AttackAdversarial Defense	CodeCode Available	0
Amoeba: Circumventing ML-supported Network Censorship via Adversarial Reinforcement Learning	Oct 31, 2023	Adversarial Attackreinforcement-learning	CodeCode Available	0
Imperceptible Face Forgery Attack via Adversarial Semantic Mask	Jun 16, 2024	Adversarial Attack	CodeCode Available	0
Adversarial Attack for RGB-Event based Visual Object Tracking	Apr 19, 2025	Adversarial AttackObject Tracking	CodeCode Available	0
Adversarial Attack and Defense on Graph Data: A Survey	Dec 26, 2018	Adversarial Attackimage-classification	CodeCode Available	0
Improved Adversarial Training Through Adaptive Instance-wise Loss Smoothing	Mar 24, 2023	Adversarial AttackAdversarial Robustness	CodeCode Available	0
Provable defenses against adversarial examples via the convex outer adversarial polytope	Nov 2, 2017	Adversarial Attack	CodeCode Available	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