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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 101–110 of 1808 papers

Title	Date	Tasks	Status	Hype
CosPGD: an efficient white-box adversarial attack for pixel-wise prediction tasks	Feb 4, 2023	Adversarial AttackAdversarial Robustness	CodeCode Available	1
On the Adversarial Robustness of Camera-based 3D Object Detection	Jan 25, 2023	3D Object DetectionAdversarial Attack	CodeCode Available	1
Boosting Adversarial Transferability via Gradient Relevance Attack	Jan 1, 2023	Adversarial Attack	CodeCode Available	1
Generalizable Black-Box Adversarial Attack with Meta Learning	Jan 1, 2023	Adversarial AttackMeta-Learning	CodeCode Available	1
RIATIG: Reliable and Imperceptible Adversarial Text-to-Image Generation With Natural Prompts	Jan 1, 2023	Adversarial AttackAdversarial Text	CodeCode Available	1
Proximal Splitting Adversarial Attack for Semantic Segmentation	Jan 1, 2023	Adversarial AttackSegmentation	CodeCode Available	1
Simultaneously Optimizing Perturbations and Positions for Black-box Adversarial Patch Attacks	Dec 26, 2022	Adversarial AttackFace Recognition	CodeCode Available	1
Discrete Point-wise Attack Is Not Enough: Generalized Manifold Adversarial Attack for Face Recognition	Dec 19, 2022	Adversarial AttackData Augmentation	CodeCode Available	1
HOTCOLD Block: Fooling Thermal Infrared Detectors with a Novel Wearable Design	Dec 12, 2022	Adversarial Attack	CodeCode Available	1
Imperceptible Adversarial Attack via Invertible Neural Networks	Nov 28, 2022	Adversarial Attack	CodeCode Available	1

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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