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 826–850 of 1808 papers

TitleStatusHype
From Sound Representation to Model Robustness—0
Adversarial Attack and Defense for LoRa Device Identification and Authentication via Deep Learning—0
GAIM: Attacking Graph Neural Networks via Adversarial Influence Maximization—0
GasHis-Transformer: A Multi-scale Visual Transformer Approach for Gastric Histopathological Image Detection—0
Hybrid Classical-Quantum Deep Learning Models for Autonomous Vehicle Traffic Image Classification Under Adversarial Attack—0
HyperAttack: Multi-Gradient-Guided White-box Adversarial Structure Attack of Hypergraph Neural Networks—0
An Adversarial Attack via Feature Contributive Regions—0
How Sampling Impacts the Robustness of Stochastic Neural Networks—0
An Adversarial Attack Defending System for Securing In-Vehicle Networks—0
Post-train Black-box Defense via Bayesian Boundary Correction—0
Adversarial Attack Type I: Cheat Classifiers by Significant Changes—0
A Computationally Efficient Method for Defending Adversarial Deep Learning Attacks—0
BB-Patch: BlackBox Adversarial Patch-Attack using Zeroth-Order Optimization—0
Homomorphic Encryption and Federated Learning based Privacy-Preserving CNN Training: COVID-19 Detection Use-Case—0
Generating Adversarial Examples with an Optimized Quality—0
Generating Adversarial Inputs Using A Black-box Differential Technique—0
Generating Black-Box Adversarial Examples in Sparse Domain—0
Benchmarking Adversarial Robustness—0
Adversarial RAW: Image-Scaling Attack Against Imaging Pipeline—0
Generating Out of Distribution Adversarial Attack using Latent Space Poisoning—0
Generating Semantic Adversarial Examples via Feature Manipulation—0
Defending Against Adversarial Examples by Regularized Deep Embedding—0
Defending against Adversarial Attack towards Deep Neural Networks via Collaborative Multi-task Training—0
I2VGuard: Safeguarding Images against Misuse in Diffusion-based Image-to-Video Models—0
Defending Against Adversarial Attack in ECG Classification with Adversarial Distillation 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