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 17261750 of 1808 papers

TitleStatusHype
Optimal Transport Classifier: Defending Against Adversarial Attacks by Regularized Deep Embedding0
CAAD 2018: Iterative Ensemble Adversarial Attack0
FAdeML: Understanding the Impact of Pre-Processing Noise Filtering on Adversarial Machine Learning0
Learning to Defend by Learning to Attack0
Unauthorized AI cannot Recognize Me: Reversible Adversarial Example0
Improved Network Robustness with Adversary CriticCode0
Adversarial Machine Learning And Speech Emotion Recognition: Utilizing Generative Adversarial Networks For Robustness0
Transferable and Configurable Audio Adversarial Attack from Low-Level Features0
The UCR Time Series ArchiveCode0
Is PGD-Adversarial Training Necessary? Alternative Training via a Soft-Quantization Network with Noisy-Natural Samples OnlyCode0
The Adversarial Attack and Detection under the Fisher Information MetricCode0
Improving the Generalization of Adversarial Training with Domain AdaptationCode0
CAAD 2018: Generating Transferable Adversarial ExamplesCode0
Using Word Embeddings to Explore the Learned Representations of Convolutional Neural Networks0
Universal Attacks on Equivariant Networks0
Adversarial Defense via Data Dependent Activation Function and Total Variation MinimizationCode0
Efficient Formal Safety Analysis of Neural NetworksCode0
Query-Efficient Black-Box Attack by Active Learning0
Isolated and Ensemble Audio Preprocessing Methods for Detecting Adversarial Examples against Automatic Speech Recognition0
Certified Adversarial Robustness with Additive NoiseCode0
Query Attack via Opposite-Direction Feature:Towards Robust Image RetrievalCode0
IDSGAN: Generative Adversarial Networks for Attack Generation against Intrusion Detection0
Adversarial Attack Type I: Cheat Classifiers by Significant Changes0
Maximal Jacobian-based Saliency Map Attack0
Stochastic Combinatorial Ensembles for Defending Against Adversarial Examples0
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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