SOTAVerified|Agents Browse Leaderboard About

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 711–720 of 1808 papers

Title	Date	Tasks	Status	Hype	Score
Exacerbating Algorithmic Bias through Fairness Attacks	Dec 16, 2020	Adversarial AttackBIG-bench Machine Learning	CodeCode Available	0	5
Hidden Activations Are Not Enough: A General Approach to Neural Network Predictions	Sep 20, 2024	Adversarial Attack	CodeCode Available	0	5
Query-Efficient Adversarial Attack Against Vertical Federated Graph Learning	Nov 5, 2024	Adversarial AttackFederated Learning	CodeCode Available	0	5
Query-Efficient Adversarial Attack Based on Latin Hypercube Sampling	Jul 5, 2022	Adversarial Attack	CodeCode Available	0	5
Combining Generators of Adversarial Malware Examples to Increase Evasion Rate	Apr 14, 2023	Adversarial Attack	CodeCode Available	0	5
Exact Adversarial Attack to Image Captioning via Structured Output Learning with Latent Variables	May 10, 2019	Adversarial AttackImage Captioning	CodeCode Available	0	5
ColorFool: Semantic Adversarial Colorization	Nov 25, 2019	Adversarial AttackColorization	CodeCode Available	0	5
Excess Capacity and Backdoor Poisoning	Sep 2, 2021	Adversarial AttackBackdoor Attack	CodeCode Available	0	5
Exploiting vulnerabilities of deep neural networks for privacy protection	Jul 19, 2020	Adversarial AttackQuantization	CodeCode Available	0	5
A Game-Based Approximate Verification of Deep Neural Networks with Provable Guarantees	Jul 10, 2018	Adversarial AttackAdversarial Defense	CodeCode Available	0	5

Show:10 25 50

← PrevPage 72 of 181Next →

All datasets CIFAR-10 CIFAR-100

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	ResNet20	Test Accuracy	89.95	89.95(1)	Community Verified
2	Xu et al.	Attack: PGD20	78.68	—	Unverified
3	3-ensemble of multi-resolution self-ensembles	Attack: AutoAttack	78.13	—	Unverified
4	TRADES-ANCRA/ResNet18	Attack: AutoAttack	59.7	—	Unverified
5	AdvTraining [madry2018]	Attack: PGD20	48.44	—	Unverified
6	TRADES [zhang2019b]	Attack: PGD20	45.9	—	Unverified
7	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