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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 1101–1110 of 1808 papers

Title	Date	Tasks	Status	Hype
The FEVER2.0 Shared Task	Nov 1, 2019	Adversarial Attack	—Unverified	0
The Impacts of Unanswerable Questions on the Robustness of Machine Reading Comprehension Models	Jan 31, 2023	Adversarial AttackMachine Reading Comprehension	—Unverified	0
There are No Bit Parts for Sign Bits in Black-Box Attacks	Feb 19, 2019	Adversarial Attack	—Unverified	0
The Relationship Between Network Similarity and Transferability of Adversarial Attacks	Jan 27, 2025	Adversarial Attack	—Unverified	0
Thundernna: a white box adversarial attack	Nov 24, 2021	Adversarial Attack	—Unverified	0
Thwarting finite difference adversarial attacks with output randomization	May 23, 2019	Adversarial Attack	—Unverified	0
Time-aware Gradient Attack on Dynamic Network Link Prediction	Nov 24, 2019	Adversarial AttackLink Prediction	—Unverified	0
To be Robust and to be Fair: Aligning Fairness with Robustness	Mar 31, 2023	Adversarial AttackFairness	—Unverified	0
To Each (Textual Sequence) Its Own: Improving Memorized-Data Unlearning in Large Language Models	May 6, 2024	Adversarial AttackMemorization	—Unverified	0
BOSH: An Efficient Meta Algorithm for Decision-based Attacks	Sep 10, 2019	Adversarial AttackBayesian Optimization	—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