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 1026–1050 of 1808 papers

TitleStatusHype
On the reversibility of adversarial attacks—0
On the Perils of Cascading Robust ClassifiersCode0
Attack-Agnostic Adversarial Detection—0
Semantic Autoencoder and Its Potential Usage for Adversarial Attack—0
Exposing Fine-Grained Adversarial Vulnerability of Face Anti-Spoofing Models—0
Unfooling Perturbation-Based Post Hoc ExplainersCode0
Superclass Adversarial Attack—0
Mixture GAN For Modulation Classification Resiliency Against Adversarial Attacks—0
Physical-World Optical Adversarial Attacks on 3D Face Recognition—0
Adversarial Body Shape Search for Legged Robots—0
Sparse Adversarial Attack in Multi-agent Reinforcement Learning—0
Transferable Physical Attack against Object Detection with Separable Attention—0
3D-VFD: A Victim-free Detector against 3D Adversarial Point Clouds—0
Learn2Weight: Parameter Adaptation against Similar-domain Adversarial Attacks—0
Btech thesis report on adversarial attack detection and purification of adverserially attacked images—0
Holistic Approach to Measure Sample-level Adversarial Vulnerability and its Utility in Building Trustworthy Systems—0
CE-based white-box adversarial attacks will not work using super-fitting—0
Rethinking Classifier and Adversarial Attack—0
Deep-Attack over the Deep Reinforcement Learning—0
BERTops: Studying BERT Representations under a Topological LensCode0
Uncertainty Estimation of Transformer Predictions for Misclassification DetectionCode0
Adversarial attacks on an optical neural network—0
Adversarial Fine-tune with Dynamically Regulated Adversary—0
An Adversarial Attack Analysis on Malicious Advertisement URL Detection FrameworkCode0
Mixed Strategies for Security Games with General Defending Requirements—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