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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 1711–1720 of 1808 papers

Title	Date	Tasks	Status	Hype	Score
Distributed Estimation over Directed Graphs Resilient to Sensor Spoofing	Apr 10, 2021	Adversarial Attack	—Unverified	0	0
A Novel Deep Learning based Model to Defend Network Intrusion Detection System against Adversarial Attacks	Jul 31, 2023	Adversarial AttackIntrusion Detection	—Unverified	0	0
Adversarial Attack Framework on Graph Embedding Models with Limited Knowledge	May 26, 2021	Adversarial AttackGraph Embedding	—Unverified	0	0
DLOVE: A new Security Evaluation Tool for Deep Learning Based Watermarking Techniques	Jul 9, 2024	Adversarial AttackImage Manipulation	—Unverified	0	0
DMS: Addressing Information Loss with More Steps for Pragmatic Adversarial Attacks	Jun 9, 2024	Adversarial Attack	—Unverified	0	0
DO-AutoEncoder: Learning and Intervening Bivariate Causal Mechanisms in Images	Sep 25, 2019	Adversarial AttackRepresentation Learning	—Unverified	0	0
DODEM: DOuble DEfense Mechanism Against Adversarial Attacks Towards Secure Industrial Internet of Things Analytics	Jan 23, 2023	Adversarial AttackNovelty Detection	—Unverified	0	0
Does Safety Training of LLMs Generalize to Semantically Related Natural Prompts?	Dec 4, 2024	Adversarial Attack	—Unverified	0	0
Domain Adaptive Transfer Attack (DATA)-based Segmentation Networks for Building Extraction from Aerial Images	Apr 11, 2020	Adversarial AttackSegmentation	—Unverified	0	0
DoPa: A Comprehensive CNN Detection Methodology against Physical Adversarial Attacks	May 21, 2019	Adversarial AttackAdversarial Attack Detection	—Unverified	0	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