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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 291–300 of 1808 papers

Title	Date	Tasks	Status	Hype	Score
Fooling Detection Alone is Not Enough: First Adversarial Attack against Multiple Object Tracking	May 27, 2019	Adversarial AttackAutonomous Driving	CodeCode Available	1	5
BASAR:Black-box Attack on Skeletal Action Recognition	Mar 9, 2021	Action RecognitionActivity Recognition	CodeCode Available	1	5
Human-in-the-Loop Generation of Adversarial Texts: A Case Study on Tibetan Script	Dec 17, 2024	Adversarial AttackAdversarial Robustness	CodeCode Available	1	5
Motion-Excited Sampler: Video Adversarial Attack with Sparked Prior	Mar 17, 2020	Adversarial AttackVideo Classification	CodeCode Available	1	5
Fooling the Image Dehazing Models by First Order Gradient	Mar 30, 2023	Adversarial AttackImage Dehazing	CodeCode Available	1	5
Improving Adversarial Transferability with Gradient Refining	May 11, 2021	Adversarial AttackDiversity	CodeCode Available	1	5
Beta-CROWN: Efficient Bound Propagation with Per-neuron Split Constraints for Complete and Incomplete Neural Network Robustness Verification	Mar 11, 2021	Adversarial Attack	CodeCode Available	1	5
Renofeation: A Simple Transfer Learning Method for Improved Adversarial Robustness	Feb 7, 2020	Adversarial AttackAdversarial Robustness	CodeCode Available	1	5
IoU Attack: Towards Temporally Coherent Black-Box Adversarial Attack for Visual Object Tracking	Mar 27, 2021	Adversarial Attackimage-classification	CodeCode Available	1	5
Pick-Object-Attack: Type-Specific Adversarial Attack for Object Detection	Jun 5, 2020	Adversarial AttackImage Captioning	CodeCode Available	1	5

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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