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 1501–1525 of 1808 papers

TitleStatusHype
Over-the-Air Adversarial Attacks on Deep Learning Based Modulation Classifier over Wireless Channels—0
DANCE: Enhancing saliency maps using decoysCode0
Practical Fast Gradient Sign Attack against Mammographic Image Classifier—0
Analyzing the Noise Robustness of Deep Neural Networks—0
Adversarial Attack on Community Detection by Hiding IndividualsCode1
Massif: Interactive Interpretation of Adversarial Attacks on Deep Learning—0
Universal Adversarial Attack on Attention and the Resulting Dataset DAmageNet—0
A Little Fog for a Large TurnCode2
Generating Semantic Adversarial Examples via Feature Manipulation—0
Implicit Euler Skip Connections: Enhancing Adversarial Robustness via Numerical Stability—0
Interpolation between CNNs and ResNets—0
Exploring Adversarial Attack in Spiking Neural Networks with Spike-Compatible Gradient—0
Fooling Detection Alone is Not Enough: Adversarial Attack against Multiple Object TrackingCode1
Benchmarking Adversarial Robustness—0
Geometry-Aware Generation of Adversarial Point CloudsCode0
T3: Tree-Autoencoder Constrained Adversarial Text Generation for Targeted AttackCode1
CAG: A Real-time Low-cost Enhanced-robustness High-transferability Content-aware Adversarial Attack Generator—0
On-manifold Adversarial Data Augmentation Improves Uncertainty Calibration—0
DAmageNet: A Universal Adversarial DatasetCode0
Potential adversarial samples for white-box attacks—0
Amora: Black-box Adversarial Morphing Attack—0
Scratch that! An Evolution-based Adversarial Attack against Neural NetworksCode0
Region-Wise Attack: On Efficient Generation of Robust Physical Adversarial Examples—0
AdvPC: Transferable Adversarial Perturbations on 3D Point CloudsCode0
Classification-by-Components: Probabilistic Modeling of Reasoning over a Set of ComponentsCode0
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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