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 12511275 of 1808 papers

TitleStatusHype
Deep Feature Space Trojan Attack of Neural Networks by Controlled DetoxificationCode1
Variational Quantum Cloning: Improving Practicality for Quantum Cryptanalysis0
Exploiting Vulnerability of Pooling in Convolutional Neural Networks by Strict Layer-Output Manipulation for Adversarial Attacks0
Blurring Fools the Network -- Adversarial Attacks by Feature Peak Suppression and Gaussian Blurring0
Efficient Training of Robust Decision Trees Against Adversarial ExamplesCode1
A Hierarchical Feature Constraint to Camouflage Medical Adversarial AttacksCode0
Exacerbating Algorithmic Bias through Fairness AttacksCode0
A Distributed Black-Box Adversarial Attack Based on Multi-Group Particle Swarm OptimizationCode0
Disentangled Information BottleneckCode1
Blindfolded Attackers Still Threatening: Strict Black-Box Adversarial Attacks on Graphs0
Attack Agnostic Detection of Adversarial Examples via Random Subspace Analysis0
Composite Adversarial AttacksCode1
SPAA: Stealthy Projector-based Adversarial Attacks on Deep Image ClassifiersCode1
Geometric Adversarial Attacks and Defenses on 3D Point CloudsCode1
Generating Out of Distribution Adversarial Attack using Latent Space Poisoning0
Using Feature Alignment Can Improve Clean Average Precision and Adversarial Robustness in Object DetectionCode1
Towards Natural Robustness Against Adversarial Examples0
FenceBox: A Platform for Defeating Adversarial Examples with Data Augmentation TechniquesCode0
Channel Effects on Surrogate Models of Adversarial Attacks against Wireless Signal Classifiers0
Enhancing Neural Models with Vulnerability via Adversarial AttackCode0
Contrastive Zero-Shot Learning for Cross-Domain Slot Filling with Adversarial Attack0
Adversarial Attacks on Deep Graph Matching0
Adversarial Learning for Robust Deep ClusteringCode1
Just One Moment: Structural Vulnerability of Deep Action Recognition against One Frame Attack0
Guided Adversarial Attack for Evaluating and Enhancing Adversarial DefensesCode1
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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