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 1376–1400 of 1808 papers

Title	Date	Tasks	Status
LSDAT: Low-Rank and Sparse Decomposition for Decision-based Adversarial Attack	Mar 19, 2021	Adversarial AttackComputational Efficiency	—Unverified
MAA: Meticulous Adversarial Attack against Vision-Language Pre-trained Models	Feb 12, 2025	Adversarial Attack	—Unverified
Make the Most of Everything: Further Considerations on Disrupting Diffusion-based Customization	Mar 18, 2025	Adversarial Attack	—Unverified
MARAGE: Transferable Multi-Model Adversarial Attack for Retrieval-Augmented Generation Data Extraction	Feb 5, 2025	Adversarial AttackRAG	—Unverified
Massif: Interactive Interpretation of Adversarial Attacks on Deep Learning	Jan 21, 2020	Adversarial AttackDeep Learning	—Unverified
MathAttack: Attacking Large Language Models Towards Math Solving Ability	Sep 4, 2023	Adversarial AttackGSM8K	—Unverified
Fast Inference of Removal-Based Node Influence	Mar 13, 2024	Adversarial Attackcounterfactual	CodeCode Available
Fast Adversarial CNN-based Perturbation Attack of No-Reference Image Quality Metrics	Apr 11, 2023	Adversarial AttackNo-Reference Image Quality Assessment	CodeCode Available
Fashion-Guided Adversarial Attack on Person Segmentation	Apr 17, 2021	Adversarial AttackHuman Instance Segmentation	CodeCode Available
FDA: Feature Disruptive Attack	Sep 10, 2019	Adversarial Attackimage-classification	CodeCode Available
A Restricted Black-box Adversarial Framework Towards Attacking Graph Embedding Models	Aug 4, 2019	Adversarial AttackGraph Embedding	CodeCode Available
Versatile Weight Attack via Flipping Limited Bits	Jul 25, 2022	Adversarial AttackBackdoor Attack	CodeCode Available
Extending Adversarial Attacks to Produce Adversarial Class Probability Distributions	Apr 14, 2020	Adversarial AttackEmotion Classification	CodeCode Available
Feature Space Perturbations Yield More Transferable Adversarial Examples	Jun 1, 2019	Adversarial Attack	CodeCode Available
The Limitations of Deep Learning in Adversarial Settings	Nov 24, 2015	Adversarial AttackAdversarial Defense	CodeCode Available
The LogBarrier adversarial attack: making effective use of decision boundary information	Mar 25, 2019	Adversarial Attackimage-classification	CodeCode Available
Parametric Noise Injection: Trainable Randomness to Improve Deep Neural Network Robustness against Adversarial Attack	Nov 22, 2018	Adversarial AttackAdversarial Defense	CodeCode Available
Federated Zeroth-Order Optimization using Trajectory-Informed Surrogate Gradients	Aug 8, 2023	Adversarial AttackFederated Learning	CodeCode Available
FenceBox: A Platform for Defeating Adversarial Examples with Data Augmentation Techniques	Dec 3, 2020	Adversarial AttackData Augmentation	CodeCode Available
Adversarial Images for Variational Autoencoders	Dec 1, 2016	Adversarial Attack	CodeCode Available
The Power of MEME: Adversarial Malware Creation with Model-Based Reinforcement Learning	Aug 31, 2023	Adversarial Attack	CodeCode Available
Are Your Explanations Reliable? Investigating the Stability of LIME in Explaining Text Classifiers by Marrying XAI and Adversarial Attack	May 21, 2023	Adversarial Attack	CodeCode Available
Patch augmentation: Towards efficient decision boundaries for neural networks	Nov 8, 2019	Adversarial Attack	CodeCode Available
FireBERT: Hardening BERT-based classifiers against adversarial attack	Aug 10, 2020	Adversarial Attack	CodeCode Available
Transferability Bound Theory: Exploring Relationship between Adversarial Transferability and Flatness	Nov 10, 2023	Adversarial AttackDiversity	CodeCode Available

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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