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 17511775 of 1808 papers

TitleStatusHype
Structured Adversarial Attack: Towards General Implementation and Better InterpretabilityCode0
Rob-GAN: Generator, Discriminator, and Adversarial AttackerCode0
Evaluating and Understanding the Robustness of Adversarial Logit PairingCode0
Harmonic Adversarial Attack Method0
With Friends Like These, Who Needs Adversaries?Code0
A Game-Based Approximate Verification of Deep Neural Networks with Provable GuaranteesCode0
Adaptive Adversarial Attack on Scene Text Recognition0
Adversarial Examples in Deep Learning: Characterization and Divergence0
Learning Visually-Grounded Semantics from Contrastive Adversarial SamplesCode0
Evaluation of Momentum Diverse Input Iterative Fast Gradient Sign Method (M-DI2-FGSM) Based Attack Method on MCS 2018 Adversarial Attacks on Black Box Face Recognition System0
Adversarial Attack on Graph Structured DataCode0
An Explainable Adversarial Robustness Metric for Deep Learning Neural Networks0
Adversarial Attacks on Face Detectors using Neural Net based Constrained Optimization0
Sequential Attacks on Agents for Long-Term Adversarial Goals0
ADAGIO: Interactive Experimentation with Adversarial Attack and Defense for Audio0
GenAttack: Practical Black-box Attacks with Gradient-Free OptimizationCode0
Knowledge Distillation with Adversarial Samples Supporting Decision BoundaryCode0
ADef: an Iterative Algorithm to Construct Adversarial DeformationsCode0
ShapeShifter: Robust Physical Adversarial Attack on Faster R-CNN Object DetectorCode0
An ADMM-Based Universal Framework for Adversarial Attacks on Deep Neural Networks0
Learn To Pay AttentionCode0
Protection against Cloning for Deep Learning0
Adversarial Defense based on Structure-to-Signal Autoencoders0
DeepGauge: Multi-Granularity Testing Criteria for Deep Learning Systems0
Improving Transferability of Adversarial Examples with Input DiversityCode0
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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