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 951–975 of 1808 papers

TitleStatusHype
Evaluation of Four Black-box Adversarial Attacks and Some Query-efficient Improvement Analysis—0
Towards Adversarially Robust Deep Image Denoising—0
Adversarially Robust Classification by Conditional Generative Model Inversion—0
Similarity-based Gray-box Adversarial Attack Against Deep Face RecognitionCode0
ROOM: Adversarial Machine Learning Attacks Under Real-Time Constraints—0
Towards Transferable Unrestricted Adversarial Examples with Minimum ChangesCode1
Towards Efficient Data Free Black-Box Adversarial AttackCode1
Bounded Adversarial Attack on Deep Content FeaturesCode0
Exploring Effective Data for Surrogate Training Towards Black-Box AttackCode1
360-Attack: Distortion-Aware Perturbations From Perspective-Views—0
Appearance and Structure Aware Robust Deep Visual Graph Matching: Attack, Defense and BeyondCode1
Adversarial Attack via Dual-Stage Network ErosionCode0
A General Framework for Evaluating Robustness of Combinatorial Optimization Solvers on Graphs—0
Adversarial Attack for Asynchronous Event-based Data—0
Task and Model Agnostic Adversarial Attack on Graph Neural NetworksCode0
Adversarial Attacks against Windows PE Malware Detection: A Survey of the State-of-the-ArtCode1
A Theoretical View of Linear Backpropagation and Its ConvergenceCode0
TASA: Twin Answer Sentences Attack for Adversarial Context Generation in Question Answering—0
Reasoning Chain Based Adversarial Attack for Multi-hop Question Answering—0
Dynamics-aware Adversarial Attack of 3D Sparse Convolution NetworkCode0
Towards Robust Neural Image Compression: Adversarial Attack and Model Finetuning—0
NOMARO: Defending against Adversarial Attacks by NOMA-Inspired Reconstruction OperationCode0
Triangle Attack: A Query-efficient Decision-based Adversarial AttackCode1
MedAttacker: Exploring Black-Box Adversarial Attacks on Risk Prediction Models in Healthcare—0
Learning to Learn Transferable AttackCode0
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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