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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 941–950 of 1808 papers

Title	Date	Tasks	Status	Hype
Redefining Machine Unlearning: A Conformal Prediction-Motivated Approach	Jan 31, 2025	Adversarial AttackConformal Prediction	—Unverified	0
Refining Adaptive Zeroth-Order Optimization at Ease	Feb 3, 2025	Adversarial Attack	—Unverified	0
Region-Wise Attack: On Efficient Generation of Robust Physical Adversarial Examples	Dec 5, 2019	Adversarial Attack	—Unverified	0
Reinforce Attack: Adversarial Attack against BERT with Reinforcement Learning	Aug 17, 2021	Adversarial AttackAdversarial Text	—Unverified	0
Reinforcement Learning Based Sparse Black-box Adversarial Attack on Video Recognition Models	Aug 29, 2021	Adversarial Attackreinforcement-learning	—Unverified	0
ReLATE: Resilient Learner Selection for Multivariate Time-Series Classification Against Adversarial Attacks	Mar 10, 2025	Adversarial AttackDeep Learning	—Unverified	0
Replace-then-Perturb: Targeted Adversarial Attacks With Visual Reasoning for Vision-Language Models	Nov 1, 2024	Adversarial AttackContrastive Learning	—Unverified	0
Residue-Based Natural Language Adversarial Attack Detection	Jan 16, 2022	Adversarial AttackAdversarial Attack Detection	—Unverified	0
Resilient and constrained consensus against adversarial attacks: A distributed MPC framework	Nov 10, 2023	Adversarial AttackAdversarial Attack Detection	—Unverified	0
Resilient Dynamic Average Consensus based on Trusted agents	Mar 14, 2023	Adversarial Attack	—Unverified	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