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 1626–1650 of 1808 papers

Title	Date	Tasks	Status
Rethinking Targeted Adversarial Attacks For Neural Machine Translation	Jul 7, 2024	Adversarial AttackMachine Translation	CodeCode Available
Learning to Learn by Zeroth-Order Oracle	Oct 21, 2019	Adversarial Attack	CodeCode Available
Learning to Learn Transferable Attack	Dec 10, 2021	Adversarial AttackData Augmentation	CodeCode Available
Learning Transferable 3D Adversarial Cloaks for Deep Trained Detectors	Apr 22, 2021	Adversarial AttackObject	CodeCode Available
Learning Transferable Adversarial Examples via Ghost Networks	Dec 9, 2018	Adversarial Attack	CodeCode Available
Learning Visually-Grounded Semantics from Contrastive Adversarial Samples	Jun 27, 2018	Adversarial AttackImage Captioning	CodeCode Available
Learn To Pay Attention	Apr 6, 2018	Adversarial AttackGeneral Classification	CodeCode Available
Structured Adversarial Attack: Towards General Implementation and Better Interpretability	Aug 5, 2018	Adversarial Attack	CodeCode Available
Rethinking the Threat and Accessibility of Adversarial Attacks against Face Recognition Systems	Jul 11, 2024	Adversarial AttackFace Recognition	CodeCode Available
Adversarial sample generation and training using geometric masks for accurate and resilient license plate character recognition	Oct 25, 2023	Adversarial AttackLicense Plate Detection	CodeCode Available
Leveraging Information Consistency in Frequency and Spatial Domain for Adversarial Attacks	Aug 22, 2024	Adversarial Attack	CodeCode Available
Adversarial Robustness for Visual Grounding of Multimodal Large Language Models	May 16, 2024	Adversarial AttackAdversarial Robustness	CodeCode Available
REGroup: Rank-aggregating Ensemble of Generative Classifiers for Robust Predictions	Jun 18, 2020	Adversarial Attack	CodeCode Available
LiDAttack: Robust Black-box Attack on LiDAR-based Object Detection	Nov 4, 2024	Adversarial Attackobject-detection	CodeCode Available
Beyond Model Interpretability: On the Faithfulness and Adversarial Robustness of Contrastive Textual Explanations	Oct 17, 2022	Adversarial AttackAdversarial Robustness	CodeCode Available
Light-weight Calibrator: a Separable Component for Unsupervised Domain Adaptation	Nov 28, 2019	Adversarial AttackDomain Adaptation	CodeCode Available
LimeAttack: Local Explainable Method for Textual Hard-Label Adversarial Attack	Aug 1, 2023	Adversarial AttackHard-label Attack	CodeCode Available
Subspace Attack: Exploiting Promising Subspaces for Query-Efficient Black-box Attacks	Jun 11, 2019	Adversarial Attack	CodeCode Available
Reverse KL-Divergence Training of Prior Networks: Improved Uncertainty and Adversarial Robustness	May 31, 2019	Adversarial AttackAdversarial Attack Detection	CodeCode Available
Adversarial Robustness Analysis of Vision-Language Models in Medical Image Segmentation	May 5, 2025	Adversarial AttackAdversarial Robustness	CodeCode Available
Towards Resilient and Secure Smart Grids against PMU Adversarial Attacks: A Deep Learning-Based Robust Data Engineering Approach	Jun 6, 2023	Adversarial AttackColor Image Denoising	CodeCode Available
Local Aggressive Adversarial Attacks on 3D Point Cloud	May 19, 2021	Adversarial AttackImage to 3D	CodeCode Available
Adversarial Purification of Information Masking	Nov 26, 2023	Adversarial AttackAdversarial Purification	CodeCode Available
Beyond Hard Samples: Robust and Effective Grammatical Error Correction with Cycle Self-Augmenting	Oct 20, 2023	Adversarial AttackGrammatical Error Correction	CodeCode Available
Disrupting Deep Uncertainty Estimation Without Harming Accuracy	Oct 26, 2021	Adversarial Attack	CodeCode Available

Show:10 25 50

← PrevPage 66 of 73Next →

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