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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 951–975 of 1808 papers

TitleStatusHype
Effective Targeted Attacks for Adversarial Self-Supervised Learning—0
Learning Transferable Adversarial Robust Representations via Multi-view Consistency—0
Beyond Model Interpretability: On the Faithfulness and Adversarial Robustness of Contrastive Textual ExplanationsCode0
Probabilistic Categorical Adversarial Attack & Adversarial Training—0
Object-Attentional Untargeted Adversarial Attack—0
Dynamics-aware Adversarial Attack of Adaptive Neural NetworksCode0
AccelAT: A Framework for Accelerating the Adversarial Training of Deep Neural Networks through Accuracy GradientCode0
Adv-Attribute: Inconspicuous and Transferable Adversarial Attack on Face Recognition—0
Adversarial Attack Against Image-Based Localization Neural Networks—0
FedDef: Defense Against Gradient Leakage in Federated Learning-based Network Intrusion Detection Systems—0
Dynamic Stochastic Ensemble with Adversarial Robust Lottery Ticket Subnetworks—0
Jitter Does Matter: Adapting Gaze Estimation to New Domains—0
A Study on the Efficiency and Generalization of Light Hybrid Retrievers—0
Robust Fair Clustering: A Novel Fairness Attack and Defense FrameworkCode0
On Attacking Out-Domain Uncertainty Estimation in Deep Neural Networks—0
PlugAT: A Plug and Play Module to Defend against Textual Adversarial Attack—0
Can We Really Trust Explanations? Evaluating the Stability of Feature Attribution Explanation Methods via Adversarial Attack—0
A Survey on Physical Adversarial Attack in Computer Vision—0
Activation Learning by Local Competitions—0
Fair Robust Active Learning by Joint Inconsistency—0
AdvDO: Realistic Adversarial Attacks for Trajectory Prediction—0
Adversarial Color Projection: A Projector-based Physical Attack to DNNs—0
Watch What You Pretrain For: Targeted, Transferable Adversarial Examples on Self-Supervised Speech Recognition modelsCode0
PointACL:Adversarial Contrastive Learning for Robust Point Clouds Representation under Adversarial AttackCode0
Robust Constrained Reinforcement Learning—0
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All datasetsCIFAR-10CIFAR-100

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Xu et al.Attack: PGD2078.68—Unverified
23-ensemble of multi-resolution self-ensemblesAttack: AutoAttack78.13—Unverified
3TRADES-ANCRA/ResNet18Attack: AutoAttack59.7—Unverified
4AdvTraining [madry2018]Attack: PGD2048.44—Unverified
5TRADES [zhang2019b]Attack: PGD2045.9—Unverified
6XU-NetRobust Accuracy1—Unverified
#ModelMetricClaimedVerifiedStatus
13-ensemble of multi-resolution self-ensemblesAttack: AutoAttack51.28—Unverified
2multi-resolution self-ensemblesAttack: AutoAttack47.85—Unverified