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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 891–900 of 1808 papers

Title	Date	Tasks	Status	Hype
HGAttack: Transferable Heterogeneous Graph Adversarial Attack	Jan 18, 2024	Adversarial Attack	—Unverified	0
Improving Gradient-based Adversarial Training for Text Classification by Contrastive Learning and Auto-Encoder	Sep 14, 2021	Adversarial AttackContrastive Learning	—Unverified	0
Hiding Backdoors within Event Sequence Data via Poisoning Attacks	Aug 20, 2023	Adversarial Attack	—Unverified	0
Improving Neural Network Robustness through Neighborhood Preserving Layers	Jan 28, 2021	Adversarial Attack	—Unverified	0
DeepGauge: Multi-Granularity Testing Criteria for Deep Learning Systems	Mar 20, 2018	Adversarial AttackDeep Learning	—Unverified	0
Boosting Adversarial Transferability for Hyperspectral Image Classification Using 3D Structure-invariant Transformation and Intermediate Feature Distance	Jun 12, 2025	Adversarial AttackHyperspectral Image Classification	—Unverified	0
Hijacking Vision-and-Language Navigation Agents with Adversarial Environmental Attacks	Dec 3, 2024	Adversarial AttackVision and Language Navigation	—Unverified	0
Holistic Approach to Measure Sample-level Adversarial Vulnerability and its Utility in Building Trustworthy Systems	May 5, 2022	Adversarial AttackKnowledge Distillation	—Unverified	0
Enhancing Transferability of Adversarial Examples with Spatial Momentum	Mar 25, 2022	Adversarial Attack	—Unverified	0
An Actor-Critic Method for Simulation-Based Optimization	Oct 31, 2021	Adversarial AttackReinforcement Learning (RL)	—Unverified	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