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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 591–600 of 1808 papers

Title	Date	Tasks	Status	Hype
Are Your Explanations Reliable? Investigating the Stability of LIME in Explaining Text Classifiers by Marrying XAI and Adversarial Attack	May 21, 2023	Adversarial Attack	CodeCode Available	0
Dynamic Transformers Provide a False Sense of Efficiency	May 20, 2023	Adversarial Attack	CodeCode Available	0
Spatial-Frequency Discriminability for Revealing Adversarial Perturbations	May 18, 2023	Adversarial AttackBlocking	CodeCode Available	0
Adversarial Amendment is the Only Force Capable of Transforming an Enemy into a Friend	May 18, 2023	Adversarial AttackAdversarial Defense	—Unverified	0
Content-based Unrestricted Adversarial Attack	May 18, 2023	Adversarial Attack	—Unverified	0
Iterative Adversarial Attack on Image-guided Story Ending Generation	May 16, 2023	Adversarial AttackAdversarial Robustness	—Unverified	0
Attacking Perceptual Similarity Metrics	May 15, 2023	Adversarial AttackExperimental Design	—Unverified	0
Diffusion Models for Imperceptible and Transferable Adversarial Attack	May 14, 2023	Adversarial Attack	CodeCode Available	2
A Black-Box Attack on Code Models via Representation Nearest Neighbor Search	May 10, 2023	Adversarial AttackClone Detection	—Unverified	0
The Best Defense is Attack: Repairing Semantics in Textual Adversarial Examples	May 6, 2023	Adversarial AttackAdversarial Defense	—Unverified	0

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All datasets CIFAR-10 CIFAR-100

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	ResNet20	Test Accuracy	89.95	89.95(1)	Community Verified
2	Xu et al.	Attack: PGD20	78.68	—	Unverified
3	3-ensemble of multi-resolution self-ensembles	Attack: AutoAttack	78.13	—	Unverified
4	TRADES-ANCRA/ResNet18	Attack: AutoAttack	59.7	—	Unverified
5	AdvTraining [madry2018]	Attack: PGD20	48.44	—	Unverified
6	TRADES [zhang2019b]	Attack: PGD20	45.9	—	Unverified
7	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