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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 101–110 of 1808 papers

Title	Date	Tasks	Status	Hype
Adversarial Attack on Large Scale Graph	Sep 8, 2020	Adversarial Attack	CodeCode Available	1
BASAR:Black-box Attack on Skeletal Action Recognition	Mar 9, 2021	Action RecognitionActivity Recognition	CodeCode Available	1
GenoArmory: A Unified Evaluation Framework for Adversarial Attacks on Genomic Foundation Models	May 16, 2025	Adversarial AttackAdversarial Defense	CodeCode Available	1
Beta-CROWN: Efficient Bound Propagation with Per-neuron Split Constraints for Complete and Incomplete Neural Network Robustness Verification	Mar 11, 2021	Adversarial Attack	CodeCode Available	1
Adversarial Attack On Yolov5 For Traffic And Road Sign Detection	May 27, 2023	Adversarial Attackobject-detection	CodeCode Available	1
Boosting Adversarial Transferability via Gradient Relevance Attack	Jan 1, 2023	Adversarial Attack	CodeCode Available	1
3D Adversarial Attacks Beyond Point Cloud	Apr 25, 2021	Adversarial Attack	CodeCode Available	1
Boosting the Transferability of Video Adversarial Examples via Temporal Translation	Oct 18, 2021	Adversarial AttackTranslation	CodeCode Available	1
Bridge the Gap Between CV and NLP! A Gradient-based Textual Adversarial Attack Framework	Oct 28, 2021	Adversarial AttackLanguage Modelling	CodeCode Available	1
Adversarial Robustness Comparison of Vision Transformer and MLP-Mixer to CNNs	Oct 6, 2021	Adversarial AttackAdversarial Robustness	CodeCode Available	1

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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