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 1551–1600 of 1808 papers

TitleStatusHype
AICAttack: Adversarial Image Captioning Attack with Attention-Based OptimizationCode0
Improving Transferability of Adversarial Examples with Input DiversityCode0
Hidden Activations Are Not Enough: A General Approach to Neural Network PredictionsCode0
Query-efficient Meta Attack to Deep Neural NetworksCode0
A Hierarchical Feature Constraint to Camouflage Medical Adversarial AttacksCode0
A Game-Based Approximate Verification of Deep Neural Networks with Provable GuaranteesCode0
A Frank-Wolfe Framework for Efficient and Effective Adversarial AttacksCode0
Sparse and Imperceptible Adversarial Attack via a Homotopy AlgorithmCode0
Enhancing Adversarial Attacks: The Similar Target MethodCode0
Injecting and removing malignant features in mammography with CycleGAN: Investigation of an automated adversarial attack using neural networksCode0
Accuracy of TextFooler black box adversarial attacks on 01 loss sign activation neural network ensembleCode0
Random Transformation of Image Brightness for Adversarial AttackCode0
Boosting Adversarial Attacks with MomentumCode0
SPARK: Spatial-aware Online Incremental Attack Against Visual TrackingCode0
Block-Sparse Adversarial Attack to Fool Transformer-Based Text ClassifiersCode0
InstructTA: Instruction-Tuned Targeted Attack for Large Vision-Language ModelsCode0
Black-Box Adversarial Attack with Transferable Model-based EmbeddingCode0
Black-box Adversarial Attacks on Network-wide Multi-step Traffic State Prediction ModelsCode0
Spear and Shield: Adversarial Attacks and Defense Methods for Model-Based Link Prediction on Continuous-Time Dynamic GraphsCode0
READ: Improving Relation Extraction from an ADversarial PerspectiveCode0
Dynamics-aware Adversarial Attack of Adaptive Neural NetworksCode0
AdvPC: Transferable Adversarial Perturbations on 3D Point CloudsCode0
Real-Time Adversarial AttacksCode0
AdvHat: Real-world adversarial attack on ArcFace Face ID systemCode0
Visual explanation of black-box model: Similarity Difference and Uniqueness (SIDU) methodCode0
Uncertainty Estimation of Transformer Predictions for Misclassification DetectionCode0
Adversarial Diffusion Attacks on Graph-based Traffic Prediction ModelsCode0
Real-world adversarial attack on MTCNN face detection systemCode0
Towards Effective and Sparse Adversarial Attack on Spiking Neural Networks via Breaking Invisible Surrogate GradientsCode0
Investigating Imperceptibility of Adversarial Attacks on Tabular Data: An Empirical AnalysisCode0
Adversarial Defense via Data Dependent Activation Function and Total Variation MinimizationCode0
Accelerating Monte Carlo Bayesian Inference via Approximating Predictive Uncertainty over SimplexCode0
Towards Evaluating the Robustness of Deep Diagnostic Models by Adversarial AttackCode0
Dynamics-aware Adversarial Attack of 3D Sparse Convolution NetworkCode0
IOI: Invisible One-Iteration Adversarial Attack on No-Reference Image- and Video-Quality MetricsCode0
Dynamically Disentangling Social Bias from Task-Oriented Representations with Adversarial AttackCode0
Dynamic Adversarial Attacks on Autonomous Driving SystemsCode0
Is AmI (Attacks Meet Interpretability) Robust to Adversarial Examples?Code0
Bitstream Collisions in Neural Image Compression via Adversarial PerturbationsCode0
Stabilized Medical Image AttacksCode0
Towards Evaluating the Robustness of Neural NetworksCode0
Reducing DNN Properties to Enable Falsification with Adversarial AttacksCode0
Is PGD-Adversarial Training Necessary? Alternative Training via a Soft-Quantization Network with Noisy-Natural Samples OnlyCode0
AdvGPS: Adversarial GPS for Multi-Agent Perception AttackCode0
advertorch v0.1: An Adversarial Robustness Toolbox based on PyTorchCode0
Adversarial Attack Vulnerability of Medical Image Analysis Systems: Unexplored FactorsCode0
A black-box adversarial attack for poisoning clusteringCode0
Understanding and Combating Robust Overfitting via Input Loss Landscape Analysis and RegularizationCode0
Statistical inference for individual fairnessCode0
Robust Smart Home Face Recognition under Starving Federated DataCode0
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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