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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 801–810 of 1808 papers

Title	Date	Tasks	Status	Hype
SegPGD: An Effective and Efficient Adversarial Attack for Evaluating and Boosting Segmentation Robustness	Jul 25, 2022	Adversarial AttackSegmentation	CodeCode Available	1
Rethinking Textual Adversarial Defense for Pre-trained Language Models	Jul 21, 2022	Adversarial AttackAdversarial Defense	—Unverified	0
Illusory Attacks: Information-Theoretic Detectability Matters in Adversarial Attacks	Jul 20, 2022	Adversarial AttackAdversarial Robustness	—Unverified	0
Defending Substitution-Based Profile Pollution Attacks on Sequential Recommenders	Jul 19, 2022	Adversarial AttackAdversarial Defense	CodeCode Available	0
Decorrelative Network Architecture for Robust Electrocardiogram Classification	Jul 19, 2022	Adversarial AttackAdversarial Defense	CodeCode Available	0
Robust Multivariate Time-Series Forecasting: Adversarial Attacks and Defense Mechanisms	Jul 19, 2022	Adversarial AttackMultivariate Time Series Forecasting	CodeCode Available	5
Prior-Guided Adversarial Initialization for Fast Adversarial Training	Jul 18, 2022	Adversarial AttackAdversarial Attack on Video Classification	CodeCode Available	1
Multi-step domain adaptation by adversarial attack to H ΔH-divergence	Jul 18, 2022	Adversarial AttackDomain Adaptation	—Unverified	0
DIMBA: Discretely Masked Black-Box Attack in Single Object Tracking	Jul 17, 2022	Adversarial AttackMiscellaneous	—Unverified	0
CARBEN: Composite Adversarial Robustness Benchmark	Jul 16, 2022	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