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
Second-Order NLP Adversarial ExamplesCode0
A Study for Universal Adversarial Attacks on Texture Recognition—0
CorrAttack: Black-box Adversarial Attack with Structured Search—0
A Deep Genetic Programming based Methodology for Art Media Classification Robust to Adversarial Perturbations—0
An alternative proof of the vulnerability of retrieval in high intrinsic dimensionality neighborhood—0
Adversarial Attacks Against Deep Learning Systems for ICD-9 Code Assignment—0
Learning to Generate Image Source-Agnostic Universal Adversarial Perturbations—0
Scalable Adversarial Attack on Graph Neural Networks with Alternating Direction Method of Multipliers—0
Adversarial Exposure Attack on Diabetic Retinopathy Imagery Grading—0
Bias Field Poses a Threat to DNN-based X-Ray Recognition—0
Learning to Attack: Towards Textual Adversarial Attacking in Real-world Situations—0
Adversarial Rain Attack and Defensive Deraining for DNN Perception—0
MultAV: Multiplicative Adversarial Videos—0
Label Smoothing and Adversarial Robustness—0
Decision-based Universal Adversarial AttackCode0
Switching Transferable Gradient Directions for Query-Efficient Black-Box Adversarial AttacksCode0
Input Hessian Regularization of Neural Networks—0
A black-box adversarial attack for poisoning clusteringCode0
Adversarial attacks on deep learning models for fatty liver disease classification by modification of ultrasound image reconstruction method—0
Adversarially Robust Neural Architectures—0
Adversarial Eigen Attack on Black-Box Models—0
SIGL: Securing Software Installations Through Deep Graph Learning—0
Point Adversarial Self Mining: A Simple Method for Facial Expression Recognition—0
An Adversarial Attack Defending System for Securing In-Vehicle Networks—0
PermuteAttack: Counterfactual Explanation of Machine Learning Credit ScorecardsCode0
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All datasetsCIFAR-10CIFAR-100

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ResNet20Test Accuracy89.9589.95(1)Community Verified
2Xu et al.Attack: PGD2078.68—Unverified
33-ensemble of multi-resolution self-ensemblesAttack: AutoAttack78.13—Unverified
4TRADES-ANCRA/ResNet18Attack: AutoAttack59.7—Unverified
5AdvTraining [madry2018]Attack: PGD2048.44—Unverified
6TRADES [zhang2019b]Attack: PGD2045.9—Unverified
7XU-NetRobust Accuracy1—Unverified
#ModelMetricClaimedVerifiedStatus
13-ensemble of multi-resolution self-ensemblesAttack: AutoAttack51.28—Unverified
2multi-resolution self-ensemblesAttack: AutoAttack47.85—Unverified