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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 1781–1790 of 1808 papers

Title	Date	Tasks	Status	Hype
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples	Feb 1, 2018	Adversarial AttackAdversarial Defense	CodeCode Available	0
Certified Defenses against Adversarial Examples	Jan 29, 2018	Adversarial AttackAdversarial Defense	CodeCode Available	0
Deflecting Adversarial Attacks with Pixel Deflection	Jan 26, 2018	Adversarial Attack	CodeCode Available	0
Query-Efficient Black-box Adversarial Examples (superceded)	Dec 19, 2017	Adversarial Attack	CodeCode Available	0
Defense against Adversarial Attacks Using High-Level Representation Guided Denoiser	Dec 8, 2017	Adversarial AttackAdversarial Defense	CodeCode Available	0
Model Extraction Warning in MLaaS Paradigm	Nov 20, 2017	Adversarial AttackBIG-bench Machine Learning	—Unverified	0
Linear system security -- detection and correction of adversarial attacks in the noise-free case	Nov 15, 2017	Adversarial Attack	—Unverified	0
Provable defenses against adversarial examples via the convex outer adversarial polytope	Nov 2, 2017	Adversarial Attack	CodeCode Available	0
Generating Natural Adversarial Examples	Oct 31, 2017	Adversarial Attackimage-classification	CodeCode Available	0
Boosting Adversarial Attacks with Momentum	Oct 17, 2017	Adversarial Attack	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