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 16761700 of 1808 papers

TitleStatusHype
TabAttackBench: A Benchmark for Adversarial Attacks on Tabular DataCode0
Determining Sequence of Image Processing Technique (IPT) to Detect Adversarial AttacksCode0
Robust Decision Trees Against Adversarial ExamplesCode0
With Friends Like These, Who Needs Adversaries?Code0
Detecting Word Sense Disambiguation Biases in Machine Translation for Model-Agnostic Adversarial AttacksCode0
Mimic and Fool: A Task Agnostic Adversarial AttackCode0
Detecting and Defending Against Adversarial Attacks on Automatic Speech Recognition via Diffusion ModelsCode0
Using BERT Encoding to Tackle the Mad-lib Attack in SMS Spam DetectionCode0
BERTops: Studying BERT Representations under a Topological LensCode0
Minimally distorted Adversarial Examples with a Fast Adaptive Boundary AttackCode0
TAPE: Assessing Few-shot Russian Language UnderstandingCode0
AccelAT: A Framework for Accelerating the Adversarial Training of Deep Neural Networks through Accuracy GradientCode0
Detecting Adversarial Perturbations in Multi-Task PerceptionCode0
Robust Fair Clustering: A Novel Fairness Attack and Defense FrameworkCode0
Robust Few-Shot Named Entity Recognition with Boundary Discrimination and Correlation PurificationCode0
BEARD: Benchmarking the Adversarial Robustness for Dataset DistillationCode0
Robustness-aware Automatic Prompt OptimizationCode0
A White-Box False Positive Adversarial Attack Method on Contrastive Loss Based Offline Handwritten Signature Verification ModelsCode0
Targeted Adversarial Attacks against Neural Machine TranslationCode0
Model-Agnostic Defense for Lane Detection against Adversarial AttackCode0
Adversarial Privacy-preserving FilterCode0
Modeling Adversarial Attack on Pre-trained Language Models as Sequential Decision MakingCode0
2D-Malafide: Adversarial Attacks Against Face Deepfake Detection SystemsCode0
Robustness of Misinformation Classification Systems to Adversarial Examples Through BeamAttackCode0
Detecting Adversarial Examples in Batches -- a geometrical approachCode0
Show:102550
← PrevPage 68 of 73Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ResNet20Test Accuracy89.9589.95(1)Community Verified
2Xu et al.Attack: PGD2078.68Unverified
33-ensemble of multi-resolution self-ensemblesAttack: AutoAttack78.13Unverified
4TRADES-ANCRA/ResNet18Attack: AutoAttack59.7Unverified
5AdvTraining [madry2018]Attack: PGD2048.44Unverified
6TRADES [zhang2019b]Attack: PGD2045.9Unverified
7XU-NetRobust Accuracy1Unverified
#ModelMetricClaimedVerifiedStatus
13-ensemble of multi-resolution self-ensemblesAttack: AutoAttack51.28Unverified
2multi-resolution self-ensemblesAttack: AutoAttack47.85Unverified