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 1721–1730 of 1808 papers

TitleStatusHype
Doppelganger Method: Breaking Role Consistency in LLM Agent via Prompt-based Transferable Adversarial Attack—0
Double Backpropagation for Training Autoencoders against Adversarial Attack—0
DIP-Watermark: A Double Identity Protection Method Based on Robust Adversarial Watermark—0
Do we need entire training data for adversarial training?—0
DRO-Augment Framework: Robustness by Synergizing Wasserstein Distributionally Robust Optimization and Data Augmentation—0
AN-GCN: An Anonymous Graph Convolutional Network Defense Against Edge-Perturbing Attack—0
D-square-B: Deep Distribution Bound for Natural-looking Adversarial Attack—0
DTA: Physical Camouflage Attacks using Differentiable Transformation Network—0
Dual Teacher Knowledge Distillation with Domain Alignment for Face Anti-spoofing—0
SSCAE: A Novel Semantic, Syntactic, and Context-Aware Natural Language Adversarial Example Generator—0
Show:102550
← PrevPage 173 of 181Next →
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