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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 691–700 of 1808 papers

Title	Date	Tasks	Status	Hype
Angelic Patches for Improving Third-Party Object Detector Performance	Jan 1, 2023	Adversarial AttackObject	CodeCode Available	0
ExploreADV: Towards exploratory attack for Neural Networks	Jan 1, 2023	Adversarial Attack	—Unverified	0
Generalizable Black-Box Adversarial Attack with Meta Learning	Jan 1, 2023	Adversarial AttackMeta-Learning	CodeCode Available	1
Tracing the Origin of Adversarial Attack for Forensic Investigation and Deterrence	Dec 31, 2022	Adversarial Attack	—Unverified	0
Simultaneously Optimizing Perturbations and Positions for Black-box Adversarial Patch Attacks	Dec 26, 2022	Adversarial AttackFace Recognition	CodeCode Available	1
Multi-head Uncertainty Inference for Adversarial Attack Detection	Dec 20, 2022	Adversarial AttackAdversarial Attack Detection	—Unverified	0
Discrete Point-wise Attack Is Not Enough: Generalized Manifold Adversarial Attack for Face Recognition	Dec 19, 2022	Adversarial AttackData Augmentation	CodeCode Available	1
AI Security for Geoscience and Remote Sensing: Challenges and Future Trends	Dec 19, 2022	Adversarial AttackBackdoor Attack	—Unverified	0
Alternating Objectives Generates Stronger PGD-Based Adversarial Attacks	Dec 15, 2022	Adversarial Attack	—Unverified	0
Adversarial Attacks and Defences for Skin Cancer Classification	Dec 13, 2022	Adversarial AttackAdversarial Defense	—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