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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 861–870 of 1808 papers

Title	Date	Tasks	Status	Hype
Revisiting DeepFool: generalization and improvement	Mar 22, 2023	Adversarial AttackAdversarial Robustness	CodeCode Available	0
Wasserstein Adversarial Examples on Univariant Time Series Data	Mar 22, 2023	Adversarial AttackTime Series	—Unverified	0
Bridging Optimal Transport and Jacobian Regularization by Optimal Trajectory for Enhanced Adversarial Defense	Mar 21, 2023	Adversarial AttackAdversarial Defense	—Unverified	0
Translate your gibberish: black-box adversarial attack on machine translation systems	Mar 20, 2023	Adversarial AttackMachine Translation	CodeCode Available	0
NoisyHate: Mining Online Human-Written Perturbations for Realistic Robustness Benchmarking of Content Moderation Models	Mar 18, 2023	Adversarial AttackBenchmarking	—Unverified	0
Resilient Dynamic Average Consensus based on Trusted agents	Mar 14, 2023	Adversarial Attack	—Unverified	0
Constrained Adversarial Learning for Automated Software Testing: a literature review	Mar 14, 2023	Adversarial Attacksoftware testing	—Unverified	0
Can Adversarial Examples Be Parsed to Reveal Victim Model Information?	Mar 13, 2023	Adversarial Attack	CodeCode Available	0
Interpreting Hidden Semantics in the Intermediate Layers of 3D Point Cloud Classification Neural Network	Mar 12, 2023	3D Point Cloud ClassificationAdversarial Attack	—Unverified	0
Adaptive Local Adversarial Attacks on 3D Point Clouds for Augmented Reality	Mar 12, 2023	Adversarial Attack	—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