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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 1501–1510 of 1808 papers

Title	Date	Tasks	Status	Hype
Breaking BERT: Understanding its Vulnerabilities for Named Entity Recognition through Adversarial Attack	Sep 23, 2021	Adversarial AttackDecision Making	CodeCode Available	0
HQA-Attack: Toward High Quality Black-Box Hard-Label Adversarial Attack on Text	Feb 2, 2024	Adversarial AttackHard-label Attack	CodeCode Available	0
Enhanced countering adversarial attacks via input denoising and feature restoring	Nov 19, 2021	Adversarial AttackDenoising	CodeCode Available	0
Efficient Robust Conformal Prediction via Lipschitz-Bounded Networks	Jun 5, 2025	Adversarial AttackComputational Efficiency	CodeCode Available	0
SignSGD with Federated Defense: Harnessing Adversarial Attacks through Gradient Sign Decoding	Feb 2, 2024	Adversarial AttackQuantization	CodeCode Available	0
Bounded Adversarial Attack on Deep Content Features	Jan 1, 2022	Adversarial Attack	CodeCode Available	0
SimAug: Learning Robust Representations from 3D Simulation for Pedestrian Trajectory Prediction in Unseen Cameras	Apr 4, 2020	Adversarial AttackAdversarial Defense	CodeCode Available	0
An Adversarial Attack Analysis on Malicious Advertisement URL Detection Framework	Apr 27, 2022	Adversarial Attack	CodeCode Available	0
Similarity-based Gray-box Adversarial Attack Against Deep Face Recognition	Jan 11, 2022	Adversarial AttackFace Recognition	CodeCode Available	0
Spatial-Frequency Discriminability for Revealing Adversarial Perturbations	May 18, 2023	Adversarial AttackBlocking	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