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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 841–850 of 1808 papers

Title	Date	Tasks	Status	Hype
Boosting Adversarial Transferability via Fusing Logits of Top-1 Decomposed Feature	May 2, 2023	Adversarial Attack	CodeCode Available	0
Attack-SAM: Towards Attacking Segment Anything Model With Adversarial Examples	May 1, 2023	Adversarial AttackAdversarial Robustness	—Unverified	0
Evaluating Adversarial Robustness on Document Image Classification	Apr 24, 2023	Adversarial AttackAdversarial Robustness	—Unverified	0
Wavelets Beat Monkeys at Adversarial Robustness	Apr 19, 2023	Adversarial AttackAdversarial Robustness	—Unverified	0
Towards the Transferable Audio Adversarial Attack via Ensemble Methods	Apr 18, 2023	Adversarial AttackAutonomous Driving	—Unverified	0
Combining Generators of Adversarial Malware Examples to Increase Evasion Rate	Apr 14, 2023	Adversarial Attack	CodeCode Available	0
RecUP-FL: Reconciling Utility and Privacy in Federated Learning via User-configurable Privacy Defense	Apr 11, 2023	Adversarial AttackAttribute	—Unverified	0
Benchmarking the Physical-world Adversarial Robustness of Vehicle Detection	Apr 11, 2023	Adversarial AttackAdversarial Robustness	—Unverified	0
Fast Adversarial CNN-based Perturbation Attack of No-Reference Image Quality Metrics	Apr 11, 2023	Adversarial AttackNo-Reference Image Quality Assessment	CodeCode Available	0
Generating Adversarial Attacks in the Latent Space	Apr 10, 2023	Adversarial AttackGenerative Adversarial Network	—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