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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 1691–1700 of 1808 papers

Title	Date	Tasks	Status	Hype
BEARD: Benchmarking the Adversarial Robustness for Dataset Distillation	Nov 14, 2024	Adversarial AttackAdversarial Robustness	CodeCode Available	0
Robustness-aware Automatic Prompt Optimization	Dec 24, 2024	Adversarial Attack	CodeCode Available	0
A White-Box False Positive Adversarial Attack Method on Contrastive Loss Based Offline Handwritten Signature Verification Models	Aug 17, 2023	Adversarial AttackStyle Transfer	CodeCode Available	0
Targeted Adversarial Attacks against Neural Machine Translation	Mar 2, 2023	Adversarial AttackMachine Translation	CodeCode Available	0
Model-Agnostic Defense for Lane Detection against Adversarial Attack	Mar 1, 2021	Adversarial AttackAutonomous Driving	CodeCode Available	0
Adversarial Privacy-preserving Filter	Jul 25, 2020	Adversarial AttackFace Recognition	CodeCode Available	0
Modeling Adversarial Attack on Pre-trained Language Models as Sequential Decision Making	May 27, 2023	Adversarial AttackDecision Making	CodeCode Available	0
2D-Malafide: Adversarial Attacks Against Face Deepfake Detection Systems	Aug 26, 2024	Adversarial AttackDeepFake Detection	CodeCode Available	0
Robustness of Misinformation Classification Systems to Adversarial Examples Through BeamAttack	Jun 30, 2025	Adversarial AttackMisinformation	CodeCode Available	0
Detecting Adversarial Examples in Batches -- a geometrical approach	Jun 17, 2022	Adversarial AttackAdversarial Attack Detection	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