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 476–500 of 1808 papers

TitleStatusHype
AdvFilter: Predictive Perturbation-aware Filtering against Adversarial Attack via Multi-domain Learning—0
Attribution for Enhanced Explanation with Transferable Adversarial eXploration—0
Bregman Linearized Augmented Lagrangian Method for Nonconvex Constrained Stochastic Zeroth-order Optimization—0
Adversarial Machine Learning And Speech Emotion Recognition: Utilizing Generative Adversarial Networks For Robustness—0
Adversarial Attack on Facial Recognition using Visible Light—0
Attribution-driven Causal Analysis for Detection of Adversarial Examples—0
Attribute-Guided Encryption with Facial Texture Masking—0
Making Corgis Important for Honeycomb Classification: Adversarial Attacks on Concept-based Explainability Tools—0
BruSLeAttack: A Query-Efficient Score-Based Black-Box Sparse Adversarial Attack—0
Btech thesis report on adversarial attack detection and purification of adverserially attacked images—0
BufferSearch: Generating Black-Box Adversarial Texts With Lower Queries—0
Adversarial Attacks and Defenses: An Interpretation Perspective—0
CAAD 2018: Iterative Ensemble Adversarial Attack—0
CAG: A Real-time Low-cost Enhanced-robustness High-transferability Content-aware Adversarial Attack Generator—0
AdvMask: A Sparse Adversarial Attack Based Data Augmentation Method for Image Classification—0
Attention, Please! Adversarial Defense via Activation Rectification and Preservation—0
Natural & Adversarial Bokeh Rendering via Circle-of-Confusion Predictive Network—0
Can the state of relevant neurons in a deep neural networks serve as indicators for detecting adversarial attacks?—0
Can We Really Trust Explanations? Evaluating the Stability of Feature Attribution Explanation Methods via Adversarial Attack—0
Adversarially Robust Neural Architectures—0
Adversarial Attack on Deep Product Quantization Network for Image Retrieval—0
CAP-GAN: Towards Adversarial Robustness with Cycle-consistent Attentional Purification—0
Capsule Neural Networks as Noise Stabilizer for Time Series Data—0
DA^3: A Distribution-Aware Adversarial Attack against Language Models—0
Darknet Traffic Classification and Adversarial Attacks—0
Show:102550
← PrevPage 20 of 73Next →
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