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 626–650 of 1808 papers

Title	Date	Tasks	Status	Score
Federated Zeroth-Order Optimization using Trajectory-Informed Surrogate Gradients	Aug 8, 2023	Adversarial AttackFederated Learning	CodeCode Available	5
Fast Inference of Removal-Based Node Influence	Mar 13, 2024	Adversarial Attackcounterfactual	CodeCode Available	5
Differentiable Adversarial Attacks for Marked Temporal Point Processes	Jan 17, 2025	Adversarial AttackPoint Processes	CodeCode Available	5
Cross-lingual Cross-temporal Summarization: Dataset, Models, Evaluation	Jun 22, 2023	Adversarial AttackNegation	CodeCode Available	5
Adversarial Attacks on Gaussian Process Bandits	Oct 16, 2021	Adversarial AttackGaussian Processes	CodeCode Available	5
FenceBox: A Platform for Defeating Adversarial Examples with Data Augmentation Techniques	Dec 3, 2020	Adversarial AttackData Augmentation	CodeCode Available	5
Extending Adversarial Attacks to Produce Adversarial Class Probability Distributions	Apr 14, 2020	Adversarial AttackEmotion Classification	CodeCode Available	5
2D-Malafide: Adversarial Attacks Against Face Deepfake Detection Systems	Aug 26, 2024	Adversarial AttackDeepFake Detection	CodeCode Available	5
Angelic Patches for Improving Third-Party Object Detector Performance	Jan 1, 2023	Adversarial AttackObject	CodeCode Available	5
Fashion-Guided Adversarial Attack on Person Segmentation	Apr 17, 2021	Adversarial AttackHuman Instance Segmentation	CodeCode Available	5
Counterfactual Explanations for Face Forgery Detection via Adversarial Removal of Artifacts	Apr 12, 2024	Adversarial Attackcounterfactual	CodeCode Available	5
Exploiting vulnerabilities of deep neural networks for privacy protection	Jul 19, 2020	Adversarial AttackQuantization	CodeCode Available	5
Explainable Graph Neural Networks Under Fire	Jun 10, 2024	Adversarial Attack	CodeCode Available	5
Adversarial Attacks on Deep Neural Networks for Time Series Classification	Mar 17, 2019	Activity RecognitionAdversarial Attack	CodeCode Available	5
Explaining Adversarial Robustness of Neural Networks from Clustering Effect Perspective	Jan 1, 2023	Adversarial AttackAdversarial Robustness	CodeCode Available	5
Exploring the Vulnerability of Natural Language Processing Models via Universal Adversarial Texts	Dec 1, 2021	Adversarial Attacktext-classification	CodeCode Available	5
Fast Adversarial CNN-based Perturbation Attack of No-Reference Image Quality Metrics	Apr 11, 2023	Adversarial AttackNo-Reference Image Quality Assessment	CodeCode Available	5
FireBERT: Hardening BERT-based classifiers against adversarial attack	Aug 10, 2020	Adversarial Attack	CodeCode Available	5
Disrupting Deep Uncertainty Estimation Without Harming Accuracy	Oct 26, 2021	Adversarial Attack	CodeCode Available	5
EvoBA: An Evolution Strategy as a Strong Baseline forBlack-Box Adversarial Attacks	Jul 12, 2021	Adversarial Attack	CodeCode Available	5
REGroup: Rank-aggregating Ensemble of Generative Classifiers for Robust Predictions	Jun 18, 2020	Adversarial Attack	CodeCode Available	5
Adversarial Attack and Defense on Graph Data: A Survey	Dec 26, 2018	Adversarial Attackimage-classification	CodeCode Available	5
Task-generalizable Adversarial Attack based on Perceptual Metric	Nov 22, 2018	Adversarial Attackobject-detection	CodeCode Available	5
Exacerbating Algorithmic Bias through Fairness Attacks	Dec 16, 2020	Adversarial AttackBIG-bench Machine Learning	CodeCode Available	5
Controversial stimuli: pitting neural networks against each other as models of human recognition	Nov 21, 2019	Adversarial Attack	CodeCode Available	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