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 1076–1100 of 1808 papers

TitleStatusHype
Excess Capacity and Backdoor PoisoningCode0
Reinforcement Learning Based Sparse Black-box Adversarial Attack on Video Recognition Models—0
DropAttack: A Masked Weight Adversarial Training Method to Improve Generalization of Neural NetworksCode1
Disrupting Adversarial Transferability in Deep Neural NetworksCode0
Improving Visual Quality of Unrestricted Adversarial Examples with Wavelet-VAE—0
OOWL500: Overcoming Dataset Collection Bias in the Wild—0
Multi-Expert Adversarial Attack Detection in Person Re-identification Using Context Inconsistency—0
A Hard Label Black-box Adversarial Attack Against Graph Neural Networks—0
AdvDrop: Adversarial Attack to DNNs by Dropping InformationCode1
Application of Adversarial Examples to Physical ECG Signals—0
Detecting and Segmenting Adversarial Graphics Patterns from Images—0
Amplitude-Phase Recombination: Rethinking Robustness of Convolutional Neural Networks in Frequency DomainCode1
Adversarial Relighting Against Face Recognition—0
Reinforce Attack: Adversarial Attack against BERT with Reinforcement Learning—0
Optical Adversarial Attack—0
AGKD-BML: Defense Against Adversarial Attack by Attention Guided Knowledge Distillation and Bi-directional Metric LearningCode1
Deep adversarial attack on target detection systems—0
Meta Gradient Adversarial AttackCode1
Robust Transfer Learning with Pretrained Language Models through Adapters—0
Poison Ink: Robust and Invisible Backdoor AttackCode1
On the Robustness of Domain Adaption to Adversarial Attacks—0
Hybrid Classical-Quantum Deep Learning Models for Autonomous Vehicle Traffic Image Classification Under Adversarial Attack—0
An Empirical Study on Adversarial Attack on NMT: Languages and Positions Matter—0
Benign Adversarial Attack: Tricking Models for Goodness—0
A Differentiable Language Model Adversarial Attack on Text Classifiers—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