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 1201–1250 of 1808 papers

TitleStatusHype
A Survey On Universal Adversarial AttackCode1
Evaluating the Robustness of Geometry-Aware Instance-Reweighted Adversarial TrainingCode0
Model-Agnostic Defense for Lane Detection against Adversarial AttackCode0
Fast Minimum-norm Adversarial Attacks through Adaptive Norm ConstraintsCode2
Graphfool: Targeted Label Adversarial Attack on Graph Embedding—0
Targeted Attack against Deep Neural Networks via Flipping Limited Weight BitsCode1
On Fast Adversarial Robustness Adaptation in Model-Agnostic Meta-LearningCode1
Fortify Machine Learning Production Systems: Detect and Classify Adversarial Attacks—0
CAP-GAN: Towards Adversarial Robustness with Cycle-consistent Attentional Purification—0
Certifiably Robust Variational Autoencoders—0
Adversarial Attack on Network Embeddings via Supervised Network PoisoningCode0
Adversarially robust deepfake media detection using fused convolutional neural network predictions—0
Enhancing Real-World Adversarial Patches through 3D Modeling of Complex Target ScenesCode0
RoBIC: A benchmark suite for assessing classifiers robustnessCode0
Exploiting epistemic uncertainty of the deep learning models to generate adversarial samples—0
Audio Adversarial Examples: Attacks Using Vocal Masks—0
Improving Neural Network Robustness through Neighborhood Preserving Layers—0
Towards Universal Physical Attacks On Cascaded Camera-Lidar 3D Object Detection Models—0
Investigating the significance of adversarial attacks and their relation to interpretability for radar-based human activity recognition systems—0
Visual explanation of black-box model: Similarity Difference and Uniqueness (SIDU) methodCode0
Towards Practical Robustness Analysis for DNNs based on PAC-Model LearningCode0
Generating Black-Box Adversarial Examples in Sparse Domain—0
Robust Reinforcement Learning on State Observations with Learned Optimal AdversaryCode1
PICA: A Pixel Correlation-based Attentional Black-box Adversarial Attack—0
Attention-Guided Black-box Adversarial Attacks with Large-Scale Multiobjective Evolutionary Optimization—0
Adversarial Interaction Attack: Fooling AI to Misinterpret Human Intentions—0
Heating up decision boundaries: isocapacitory saturation, adversarial scenarios and generalization bounds—0
Untargeted, Targeted and Universal Adversarial Attacks and Defenses on Time Series—0
Robustness of on-device Models: Adversarial Attack to Deep Learning Models on Android AppsCode1
Random Transformation of Image Brightness for Adversarial AttackCode0
Exploring Adversarial Fake Images on Face Manifold—0
Adversarial Attack Attribution: Discovering Attributable Signals in Adversarial ML Attacks—0
Robust Text CAPTCHAs Using Adversarial Examples—0
Local Competition and Stochasticity for Adversarial Robustness in Deep Learning—0
Towards Robustness of Deep Neural Networks via Regularization—0
Meta-Attack: Class-Agnostic and Model-Agnostic Physical Adversarial Attack—0
Adversarial Attack on Deep Cross-Modal Hamming Retrieval—0
Consistency-Sensitivity Guided Ensemble Black-Box Adversarial Attacks in Low-Dimensional Spaces—0
Adversarial Example Detection Using Latent Neighborhood Graph—0
Stabilized Medical Attacks—0
Learn2Weight: Weights Transfer Defense against Similar-domain Adversarial Attacks—0
Identifying Informative Latent Variables Learned by GIN via Mutual Information—0
An Adversarial Attack via Feature Contributive Regions—0
Practical Order Attack in Deep Ranking—0
Black-Box Adversarial Attacks on Graph Neural Networks as An Influence Maximization Problem—0
AT-GAN: An Adversarial Generative Model for Non-constrained Adversarial Examples—0
Patch-wise++ Perturbation for Adversarial Targeted AttacksCode1
Black-box Adversarial Attacks on Monocular Depth Estimation Using Evolutionary Multi-objective Optimization—0
Sparse Adversarial Attack to Object DetectionCode1
Adjust-free adversarial example generation in speech recognition using evolutionary multi-objective optimization under black-box condition—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