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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 941–950 of 1808 papers

Title	Date	Tasks	Status	Hype
Robust Smart Home Face Recognition under Starving Federated Data	Nov 10, 2022	Adversarial AttackFace Recognition	CodeCode Available	0
Contrastive Weighted Learning for Near-Infrared Gaze Estimation	Nov 6, 2022	Adversarial AttackContrastive Learning	—Unverified	0
Logits are predictive of network type	Nov 4, 2022	Adversarial AttackVocal Bursts Type Prediction	CodeCode Available	0
Symmetric Saliency-based Adversarial Attack To Speaker Identification	Oct 30, 2022	Adversarial AttackDecoder	—Unverified	0
Improving the Transferability of Adversarial Attacks on Face Recognition with Beneficial Perturbation Feature Augmentation	Oct 28, 2022	Adversarial AttackFace Recognition	—Unverified	0
TASA: Deceiving Question Answering Models by Twin Answer Sentences Attack	Oct 27, 2022	Adversarial AttackQuestion Answering	CodeCode Available	0
LP-BFGS attack: An adversarial attack based on the Hessian with limited pixels	Oct 26, 2022	Adversarial Attack	CodeCode Available	0
A White-Box Adversarial Attack Against a Digital Twin	Oct 25, 2022	Adversarial Attack	—Unverified	0
TAPE: Assessing Few-shot Russian Language Understanding	Oct 23, 2022	Adversarial AttackAdversarial Text	CodeCode Available	0
Similarity of Neural Architectures using Adversarial Attack Transferability	Oct 20, 2022	Adversarial AttackDiversity	—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