SOTAVerified

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

Showing 801825 of 1808 papers

TitleStatusHype
SegPGD: An Effective and Efficient Adversarial Attack for Evaluating and Boosting Segmentation RobustnessCode1
Rethinking Textual Adversarial Defense for Pre-trained Language Models0
Illusory Attacks: Information-Theoretic Detectability Matters in Adversarial Attacks0
Defending Substitution-Based Profile Pollution Attacks on Sequential RecommendersCode0
Decorrelative Network Architecture for Robust Electrocardiogram ClassificationCode0
Robust Multivariate Time-Series Forecasting: Adversarial Attacks and Defense MechanismsCode5
Prior-Guided Adversarial Initialization for Fast Adversarial TrainingCode1
Multi-step domain adaptation by adversarial attack to H ΔH-divergence0
DIMBA: Discretely Masked Black-Box Attack in Single Object Tracking0
CARBEN: Composite Adversarial Robustness BenchmarkCode1
Adversarial Examples for Model-Based Control: A Sensitivity Analysis0
Perturbation Inactivation Based Adversarial Defense for Face RecognitionCode1
Frequency Domain Model Augmentation for Adversarial AttackCode1
How many perturbations break this model? Evaluating robustness beyond adversarial accuracyCode0
On the Relationship Between Adversarial Robustness and Decision Region in Deep Neural Network0
Query-Efficient Adversarial Attack Based on Latin Hypercube SamplingCode0
Learning to Accelerate Approximate Methods for Solving Integer Programming via Early FixingCode0
RAF: Recursive Adversarial Attacks on Face Recognition Using Extremely Limited Queries0
Resilience of Named Entity Recognition Models under Adversarial AttackCode0
SHARP: Search-Based Adversarial Attack for Structured Prediction0
ZhichunRoad at SemEval-2022 Task 2: Adversarial Training and Contrastive Learning for Multiword Representations0
BadHash: Invisible Backdoor Attacks against Deep Hashing with Clean LabelCode1
Robustness of Explanation Methods for NLP Models0
Adversarial Zoom Lens: A Novel Physical-World Attack to DNNs0
A Framework for Understanding Model Extraction Attack and Defense0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Xu et al.Attack: PGD2078.68Unverified
23-ensemble of multi-resolution self-ensemblesAttack: AutoAttack78.13Unverified
3TRADES-ANCRA/ResNet18Attack: AutoAttack59.7Unverified
4AdvTraining [madry2018]Attack: PGD2048.44Unverified
5TRADES [zhang2019b]Attack: PGD2045.9Unverified
6XU-NetRobust Accuracy1Unverified
#ModelMetricClaimedVerifiedStatus
13-ensemble of multi-resolution self-ensemblesAttack: AutoAttack51.28Unverified
2multi-resolution self-ensemblesAttack: AutoAttack47.85Unverified