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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 961–970 of 1808 papers

Title	Date	Tasks	Status	Hype
Appearance and Structure Aware Robust Deep Visual Graph Matching: Attack, Defense and Beyond	Jan 1, 2022	Adversarial AttackData Augmentation	CodeCode Available	1
Adversarial Attack via Dual-Stage Network Erosion	Jan 1, 2022	Adversarial Attack	CodeCode Available	0
A General Framework for Evaluating Robustness of Combinatorial Optimization Solvers on Graphs	Dec 28, 2021	Adversarial AttackCombinatorial Optimization	—Unverified	0
Adversarial Attack for Asynchronous Event-based Data	Dec 27, 2021	Adversarial AttackAutonomous Driving	—Unverified	0
Task and Model Agnostic Adversarial Attack on Graph Neural Networks	Dec 25, 2021	Adversarial AttackQ-Learning	CodeCode Available	0
Adversarial Attacks against Windows PE Malware Detection: A Survey of the State-of-the-Art	Dec 23, 2021	Adversarial AttackMalware Detection	CodeCode Available	1
A Theoretical View of Linear Backpropagation and Its Convergence	Dec 21, 2021	Adversarial Attack	CodeCode Available	0
TASA: Twin Answer Sentences Attack for Adversarial Context Generation in Question Answering	Dec 17, 2021	Adversarial AttackQuestion Answering	—Unverified	0
Reasoning Chain Based Adversarial Attack for Multi-hop Question Answering	Dec 17, 2021	Adversarial AttackMulti-hop Question Answering	—Unverified	0
Dynamics-aware Adversarial Attack of 3D Sparse Convolution Network	Dec 17, 2021	3D Classification3D Semantic Segmentation	CodeCode Available	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