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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 61–70 of 1808 papers

Title	Date	Tasks	Status	Hype	Score
T3: Tree-Autoencoder Constrained Adversarial Text Generation for Targeted Attack	Dec 22, 2019	Adversarial AttackAdversarial Text	CodeCode Available	1	5
Adversarial Examples in Deep Learning for Multivariate Time Series Regression	Sep 24, 2020	Adversarial AttackDeep Learning	CodeCode Available	1	5
Adversarial Attack on Large Scale Graph	Sep 8, 2020	Adversarial Attack	CodeCode Available	1	5
Guardians of Image Quality: Benchmarking Defenses Against Adversarial Attacks on Image Quality Metrics	Aug 2, 2024	Adversarial AttackAdversarial Purification	CodeCode Available	1	5
Adversarial Self-Supervised Contrastive Learning	Jun 13, 2020	Adversarial AttackContrastive Learning	CodeCode Available	1	5
Adversarial Vulnerabilities in Large Language Models for Time Series Forecasting	Dec 11, 2024	Adversarial AttackTime Series	CodeCode Available	1	5
AGKD-BML: Defense Against Adversarial Attack by Attention Guided Knowledge Distillation and Bi-directional Metric Learning	Aug 13, 2021	Adversarial AttackAdversarial Robustness	CodeCode Available	1	5
Adversarial Attack on Community Detection by Hiding Individuals	Jan 22, 2020	Adversarial AttackCommunity Detection	CodeCode Available	1	5
Adversarial Attack on Attackers: Post-Process to Mitigate Black-Box Score-Based Query Attacks	May 24, 2022	Adversarial Attack	CodeCode Available	1	5
Adversarial Attack on Deep Learning-Based Splice Localization	Apr 17, 2020	Adversarial AttackAdversarial Robustness	CodeCode Available	1	5

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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