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 1401–1425 of 1808 papers

TitleStatusHype
Deep Learning Defenses Against Adversarial Examples for Dynamic Risk Assessment—0
Query-Free Adversarial Transfer via Undertrained Surrogates—0
Determining Sequence of Image Processing Technique (IPT) to Detect Adversarial AttacksCode0
Generating Adversarial Examples with an Optimized Quality—0
RayS: A Ray Searching Method for Hard-label Adversarial AttackCode1
Adversarial Attacks for Multi-view Deep Models—0
Differentiable Language Model Adversarial Attacks on Categorical Sequence ClassifiersCode1
Local Competition and Uncertainty for Adversarial Robustness in Deep Learning—0
REGroup: Rank-aggregating Ensemble of Generative Classifiers for Robust PredictionsCode0
OGAN: Disrupting Deepfakes with an Adversarial Attack that Survives Training—0
Classifier-independent Lower-Bounds for Adversarial Robustness—0
Boosting Black-Box Attack with Partially Transferred Conditional Adversarial DistributionCode1
Adversarial Self-Supervised Contrastive LearningCode1
Targeted Adversarial Perturbations for Monocular Depth PredictionCode1
D-square-B: Deep Distribution Bound for Natural-looking Adversarial Attack—0
On the Tightness of Semidefinite Relaxations for Certifying Robustness to Adversarial Examples—0
Adversarial Attack Vulnerability of Medical Image Analysis Systems: Unexplored FactorsCode0
Interpolation between Residual and Non-Residual NetworksCode1
Global Robustness Verification Networks—0
Pick-Object-Attack: Type-Specific Adversarial Attack for Object DetectionCode1
One-Shot Adversarial Attacks on Visual Tracking With Dual Attention—0
Robust Superpixel-Guided Attentional Adversarial Attack—0
What Machines See Is Not What They Get: Fooling Scene Text Recognition Models With Adversarial Text Images—0
Modeling Biological Immunity to Adversarial Examples—0
Benchmarking Adversarial Robustness on Image ClassificationCode1
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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