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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 171–180 of 1808 papers

Title	Date	Tasks	Status	Hype	Score
Ad2Attack: Adaptive Adversarial Attack on Real-Time UAV Tracking	Mar 3, 2022	Adversarial AttackObject Tracking	CodeCode Available	1	5
An integrated Auto Encoder-Block Switching defense approach to prevent adversarial attacks	Mar 11, 2022	Adversarial AttackBIG-bench Machine Learning	CodeCode Available	1	5
High Frequency Component Helps Explain the Generalization of Convolutional Neural Networks	May 28, 2019	Adversarial AttackVocal Bursts Intensity Prediction	CodeCode Available	1	5
Anti-Adversarially Manipulated Attributions for Weakly and Semi-Supervised Semantic Segmentation	Mar 16, 2021	Adversarial AttackObject	CodeCode Available	1	5
Appearance and Structure Aware Robust Deep Visual Graph Matching: Attack, Defense and Beyond	Jan 1, 2022	Adversarial AttackData Augmentation	CodeCode Available	1	5
An Adaptive Model Ensemble Adversarial Attack for Boosting Adversarial Transferability	Aug 5, 2023	Adversarial Attack	CodeCode Available	1	5
Strong Transferable Adversarial Attacks via Ensembled Asymptotically Normal Distribution Learning	Sep 24, 2022	Adversarial Attack	CodeCode Available	1	5
Adversarial Immunization for Certifiable Robustness on Graphs	Jul 19, 2020	Adversarial AttackBilevel Optimization	CodeCode Available	1	5
Are AlphaZero-like Agents Robust to Adversarial Perturbations?	Nov 7, 2022	Adversarial AttackBoard Games	CodeCode Available	1	5
Adversarial Laser Beam: Effective Physical-World Attack to DNNs in a Blink	Mar 11, 2021	Adversarial Attack	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