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 776–800 of 1808 papers

TitleStatusHype
CAAD 2018: Iterative Ensemble Adversarial Attack—0
Adversarial Attacks for Multi-view Deep Models—0
Natural & Adversarial Bokeh Rendering via Circle-of-Confusion Predictive Network—0
BufferSearch: Generating Black-Box Adversarial Texts With Lower Queries—0
Btech thesis report on adversarial attack detection and purification of adverserially attacked images—0
AdvHaze: Adversarial Haze Attack—0
BruSLeAttack: A Query-Efficient Score-Based Black-Box Sparse Adversarial Attack—0
Making Corgis Important for Honeycomb Classification: Adversarial Attacks on Concept-based Explainability Tools—0
Adversarial Attacks and Mitigation for Anomaly Detectors of Cyber-Physical Systems—0
Brightness-Restricted Adversarial Attack Patch—0
Bridge the Gap Between CV and NLP! A Gradient-based Textual Adversarial Attack Framework—0
AdvGen: Physical Adversarial Attack on Face Presentation Attack Detection Systems—0
Adversarial Attacks and Dimensionality in Text Classifiers—0
Adv-Attribute: Inconspicuous and Transferable Adversarial Attack on Face Recognition—0
Unsourced Adversarial CAPTCHA: A Bi-Phase Adversarial CAPTCHA Framework—0
Bregman Linearized Augmented Lagrangian Method for Nonconvex Constrained Stochastic Zeroth-order Optimization—0
Breaking the False Sense of Security in Backdoor Defense through Re-Activation Attack—0
AdvFilter: Predictive Perturbation-aware Filtering against Adversarial Attack via Multi-domain Learning—0
Adverseness vs. Equilibrium: Exploring Graph Adversarial Resilience through Dynamic Equilibrium—0
Boosting Decision-Based Black-Box Adversarial Attack with Gradient Priors—0
Adversary for Social Good: Leveraging Adversarial Attacks to Protect Personal Attribute Privacy—0
Boosting Black-Box Adversarial Attacks with Meta Learning—0
Adversarial Zoom Lens: A Novel Physical-World Attack to DNNs—0
Boosting Adversarial Transferability via High-Frequency Augmentation and Hierarchical-Gradient Fusion—0
Boosting Adversarial Transferability using Dynamic Cues—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