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 1201–1225 of 1808 papers

TitleStatusHype
Differential Privacy in Personalized Pricing with Nonparametric Demand Models—0
Energy Attack: On Transferring Adversarial Examples—0
Protein Folding Neural Networks Are Not Robust—0
Membership Inference Attacks Against Temporally Correlated Data in Deep Reinforcement Learning—0
Training Meta-Surrogate Model for Transferable Adversarial AttackCode0
Utilizing Adversarial Targeted Attacks to Boost Adversarial Robustness—0
Real-World Adversarial Examples involving Makeup Application—0
Excess Capacity and Backdoor PoisoningCode0
Reinforcement Learning Based Sparse Black-box Adversarial Attack on Video Recognition Models—0
Disrupting Adversarial Transferability in Deep Neural NetworksCode0
Improving Visual Quality of Unrestricted Adversarial Examples with Wavelet-VAE—0
OOWL500: Overcoming Dataset Collection Bias in the Wild—0
Multi-Expert Adversarial Attack Detection in Person Re-identification Using Context Inconsistency—0
A Hard Label Black-box Adversarial Attack Against Graph Neural Networks—0
Detecting and Segmenting Adversarial Graphics Patterns from Images—0
Application of Adversarial Examples to Physical ECG Signals—0
Adversarial Relighting Against Face Recognition—0
Reinforce Attack: Adversarial Attack against BERT with Reinforcement Learning—0
Optical Adversarial Attack—0
Deep adversarial attack on target detection systems—0
Robust Transfer Learning with Pretrained Language Models through Adapters—0
On the Robustness of Domain Adaption to Adversarial Attacks—0
Hybrid Classical-Quantum Deep Learning Models for Autonomous Vehicle Traffic Image Classification Under Adversarial Attack—0
An Empirical Study on Adversarial Attack on NMT: Languages and Positions Matter—0
Benign Adversarial Attack: Tricking Models for Goodness—0
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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