SOTAVerified|Agents Browse Leaderboard About Blog

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 1701–1710 of 1808 papers

Title	Date	Tasks	Status	Hype
Availability Adversarial Attack and Countermeasures for Deep Learning-based Load Forecasting	Jan 4, 2023	Adversarial AttackLoad Forecasting	CodeCode Available	0
Adversarial-Playground: A Visualization Suite Showing How Adversarial Examples Fool Deep Learning	Aug 1, 2017	Adversarial AttackAdversarial Defense	CodeCode Available	0
DeSparsify: Adversarial Attack Against Token Sparsification Mechanisms in Vision Transformers	Feb 4, 2024	Adversarial AttackGPU	CodeCode Available	0
Adversarial Attacks on Parts of Speech: An Empirical Study in Text-to-Image Generation	Sep 21, 2024	Adversarial AttackImage Generation	CodeCode Available	0
Robust Overfitting Does Matter: Test-Time Adversarial Purification With FGSM	Mar 18, 2024	Adversarial AttackAdversarial Defense	CodeCode Available	0
Targeted Mismatch Adversarial Attack: Query with a Flower to Retrieve the Tower	Aug 24, 2019	Adversarial AttackRetrieval	CodeCode Available	0
Delving into Transferable Adversarial Examples and Black-box Attacks	Nov 8, 2016	Adversarial AttackAdversarial Defense	CodeCode Available	0
A Uniform Framework for Anomaly Detection in Deep Neural Networks	Oct 6, 2021	Adversarial AttackAnomaly Detection	CodeCode Available	0
Robust Reinforcement Learning under model misspecification	Mar 29, 2021	Adversarial Attackmodel	CodeCode Available	0
Enhancing Robust Representation in Adversarial Training: Alignment and Exclusion Criteria	Oct 5, 2023	Adversarial AttackAdversarial Defense	CodeCode Available	0

Show:10 25 50

← PrevPage 171 of 181Next →

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