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 1726–1750 of 1808 papers

TitleStatusHype
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
SSCAE -- Semantic, Syntactic, and Context-aware natural language Adversarial Examples generator—0
SSMI: How to Make Objects of Interest Disappear without Accessing Object Detectors?—0
Dynamic backdoor attacks against federated learning—0
Dynamic ensemble selection based on Deep Neural Network Uncertainty Estimation for Adversarial Robustness—0
Dynamic Knowledge Graph-based Dialogue Generation with Improved Adversarial Meta-Learning—0
STA: Adversarial Attacks on Siamese Trackers—0
STAA-Net: A Sparse and Transferable Adversarial Attack for Speech Emotion Recognition—0
Dynamic Stochastic Ensemble with Adversarial Robust Lottery Ticket Subnetworks—0
Stabilized Medical Attacks—0
A Bayes-Optimal View on Adversarial Examples—0
A Non-monotonic Smooth Activation Function—0
Effective black box adversarial attack with handcrafted kernels—0
Effective faking of verbal deception detection with target-aligned adversarial attacks—0
Effects of Forward Error Correction on Communications Aware Evasion Attacks—0
Efficient and Effective Universal Adversarial Attack against Vision-Language Pre-training Models—0
Stabilizing Deep Tomographic Reconstruction—0
Anomaly Detection in Unsupervised Surveillance Setting Using Ensemble of Multimodal Data with Adversarial Defense—0
Adversarial Attack for Uncertainty Estimation: Identifying Critical Regions in Neural Networks—0
Adversarial Attack for Explanation Robustness of Rationalization Models—0
An Incremental Gray-box Physical Adversarial Attack on Neural Network Training—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