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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 1451–1460 of 1808 papers

Title	Date	Tasks	Status	Hype
Can Adversarial Examples Be Parsed to Reveal Victim Model Information?	Mar 13, 2023	Adversarial Attack	CodeCode Available	0
Another Dead End for Morphological Tags? Perturbed Inputs and Parsing	May 24, 2023	Adversarial Attack	CodeCode Available	0
Adversarial Attack on Network Embeddings via Supervised Network Poisoning	Feb 14, 2021	Adversarial AttackLink Prediction	CodeCode Available	0
Evaluating and Understanding the Robustness of Adversarial Logit Pairing	Jul 26, 2018	Adversarial Attack	CodeCode Available	0
Geometry-Aware Generation of Adversarial Point Clouds	Dec 24, 2019	Adversarial AttackFairness	CodeCode Available	0
Semantic-Aware Adversarial Training for Reliable Deep Hashing Retrieval	Oct 23, 2023	Adversarial AttackAdversarial Robustness	CodeCode Available	0
CAMOU: Learning Physical Vehicle Camouflages to Adversarially Attack Detectors in the Wild	May 1, 2019	Adversarial AttackObject	CodeCode Available	0
Gotta Catch 'Em All: Using Honeypots to Catch Adversarial Attacks on Neural Networks	Apr 18, 2019	Adversarial AttackAdversarial Attack Detection	CodeCode Available	0
Angelic Patches for Improving Third-Party Object Detector Performance	Jan 1, 2023	Adversarial AttackObject	CodeCode Available	0
CAAD 2018: Generating Transferable Adversarial Examples	Sep 29, 2018	Adversarial AttackAdversarial Defense	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