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 1681–1690 of 1808 papers

TitleStatusHype
Detecting and Segmenting Adversarial Graphics Patterns from Images—0
A Sweet Rabbit Hole by DARCY: Using Honeypots to Detect Universal Trigger's Adversarial Attacks—0
Sparse Adversarial Attack in Multi-agent Reinforcement Learning—0
Adversarial Attack on Facial Recognition using Visible Light—0
DeTrigger: A Gradient-Centric Approach to Backdoor Attack Mitigation in Federated Learning—0
Device-aware Optical Adversarial Attack for a Portable Projector-camera System—0
DFT-Based Adversarial Attack Detection in MRI Brain Imaging: Enhancing Diagnostic Accuracy in Alzheimer's Case Studies—0
A Relaxed Optimization Approach for Adversarial Attacks against Neural Machine Translation Models—0
Architecture Selection via the Trade-off Between Accuracy and Robustness—0
Adversarial Attack on Deep Product Quantization Network for Image Retrieval—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