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 1701–1750 of 1808 papers

TitleStatusHype
Adversarial Metric Attack and Defense for Person Re-identificationCode0
Strong Black-box Adversarial Attacks on Unsupervised Machine Learning Models—0
Weighted-Sampling Audio Adversarial Example Attack—0
Hessian-Aware Zeroth-Order Optimization for Black-Box Adversarial Attack—0
Seeing isn't Believing: Practical Adversarial Attack Against Object Detectors—0
Adversarial Attack and Defense on Graph Data: A SurveyCode0
A Survey of Safety and Trustworthiness of Deep Neural Networks: Verification, Testing, Adversarial Attack and Defence, and Interpretability—0
Trust Region Based Adversarial Attack on Neural NetworksCode0
Learning Transferable Adversarial Examples via Ghost NetworksCode0
Deep-RBF Networks Revisited: Robust Classification with Rejection—0
Fooling Network Interpretation in Image Classification—0
Towards Leveraging the Information of Gradients in Optimization-based Adversarial Attack—0
Prior Networks for Detection of Adversarial Attacks—0
SADA: Semantic Adversarial Diagnostic Attacks for Autonomous ApplicationsCode0
FineFool: Fine Object Contour Attack via Attention—0
Attacks on State-of-the-Art Face Recognition using Attentional Adversarial Attack Generative Network—0
Adversarial Machine Learning And Speech Emotion Recognition: Utilizing Generative Adversarial Networks For Robustness—0
Adversarial Attacks for Optical Flow-Based Action Recognition Classifiers—0
A Frank-Wolfe Framework for Efficient and Effective Adversarial AttacksCode0
ResNets Ensemble via the Feynman-Kac Formalism to Improve Natural and Robust AccuraciesCode0
Attention, Please! Adversarial Defense via Activation Rectification and Preservation—0
Parametric Noise Injection: Trainable Randomness to Improve Deep Neural Network Robustness against Adversarial AttackCode0
Task-generalizable Adversarial Attack based on Perceptual MetricCode0
Intermediate Level Adversarial Attack for Enhanced Transferability—0
Injecting and removing malignant features in mammography with CycleGAN: Investigation of an automated adversarial attack using neural networksCode0
Optimal Transport Classifier: Defending Against Adversarial Attacks by Regularized Deep Embedding—0
CAAD 2018: Iterative Ensemble Adversarial Attack—0
FAdeML: Understanding the Impact of Pre-Processing Noise Filtering on Adversarial Machine Learning—0
Learning to Defend by Learning to Attack—0
Unauthorized AI cannot Recognize Me: Reversible Adversarial Example—0
Improved Network Robustness with Adversary CriticCode0
Adversarial Machine Learning And Speech Emotion Recognition: Utilizing Generative Adversarial Networks For Robustness—0
Transferable and Configurable Audio Adversarial Attack from Low-Level Features—0
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 Networks—0
Universal Attacks on Equivariant Networks—0
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 Learning—0
Isolated and Ensemble Audio Preprocessing Methods for Detecting Adversarial Examples against Automatic Speech Recognition—0
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 Detection—0
Adversarial Attack Type I: Cheat Classifiers by Significant Changes—0
Maximal Jacobian-based Saliency Map Attack—0
Stochastic Combinatorial Ensembles for Defending Against Adversarial Examples—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