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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 1791–1800 of 1808 papers

Title	Date	Tasks	Status	Hype
Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks	Oct 3, 2019	Adversarial AttackDecision Making	CodeCode Available	0
Scratch that! An Evolution-based Adversarial Attack against Neural Networks	Dec 5, 2019	Adversarial AttackImage Captioning	CodeCode Available	0
CT-GAT: Cross-Task Generative Adversarial Attack based on Transferability	Oct 22, 2023	Adversarial Attack	CodeCode Available	0
Universalization of any adversarial attack using very few test examples	May 18, 2020	Adversarial Attack	CodeCode Available	0
Query Attack via Opposite-Direction Feature:Towards Robust Image Retrieval	Sep 7, 2018	Adversarial AttackGeneral Classification	CodeCode Available	0
Word-level Textual Adversarial Attacking as Combinatorial Optimization	Oct 27, 2019	Adversarial AttackCombinatorial Optimization	CodeCode Available	0
Watch What You Pretrain For: Targeted, Transferable Adversarial Examples on Self-Supervised Speech Recognition models	Sep 17, 2022	Adversarial AttackAutomatic Speech Recognition	CodeCode Available	0
Certified Adversarial Robustness with Additive Noise	Sep 10, 2018	Adversarial AttackAdversarial Robustness	CodeCode Available	0
Adversarial Attacks on Deep Neural Networks for Time Series Classification	Mar 17, 2019	Activity RecognitionAdversarial Attack	CodeCode Available	0
Second-Order NLP Adversarial Examples	Oct 5, 2020	Adversarial AttackSemantic Similarity	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