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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 601–610 of 1808 papers

Title	Date	Tasks	Status	Hype
White-Box Multi-Objective Adversarial Attack on Dialogue Generation	May 5, 2023	Adversarial AttackDecision Making	CodeCode Available	1
New Adversarial Image Detection Based on Sentiment Analysis	May 3, 2023	Adversarial AttackGPU	CodeCode Available	0
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
Fast Adversarial CNN-based Perturbation Attack of No-Reference Image Quality Metrics	Apr 11, 2023	Adversarial AttackNo-Reference Image Quality Assessment	CodeCode Available	0
Benchmarking the Physical-world Adversarial Robustness of Vehicle Detection	Apr 11, 2023	Adversarial AttackAdversarial Robustness	—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