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 1451–1475 of 1808 papers

TitleStatusHype
Adversarial Machine Learning Attacks and Defense Methods in the Cyber Security Domain—0
Deep Learning Defenses Against Adversarial Examples for Dynamic Risk Assessment—0
Determining Sequence of Image Processing Technique (IPT) to Detect Adversarial AttacksCode0
Query-Free Adversarial Transfer via Undertrained Surrogates—0
Generating Adversarial Examples with an Optimized Quality—0
Adversarial Attacks for Multi-view Deep Models—0
Local Competition and Uncertainty for Adversarial Robustness in Deep Learning—0
REGroup: Rank-aggregating Ensemble of Generative Classifiers for Robust PredictionsCode0
OGAN: Disrupting Deepfakes with an Adversarial Attack that Survives Training—0
Classifier-independent Lower-Bounds for Adversarial Robustness—0
D-square-B: Deep Distribution Bound for Natural-looking Adversarial Attack—0
Adversarial Attack Vulnerability of Medical Image Analysis Systems: Unexplored FactorsCode0
On the Tightness of Semidefinite Relaxations for Certifying Robustness to Adversarial Examples—0
Global Robustness Verification Networks—0
What Machines See Is Not What They Get: Fooling Scene Text Recognition Models With Adversarial Text Images—0
ILFO: Adversarial Attack on Adaptive Neural Networks—0
Modeling Biological Immunity to Adversarial Examples—0
One-Shot Adversarial Attacks on Visual Tracking With Dual Attention—0
Polishing Decision-Based Adversarial Noise With a Customized Sampling—0
Robust Superpixel-Guided Attentional Adversarial Attack—0
Evaluations and Methods for Explanation through Robustness Analysis—0
Effects of Forward Error Correction on Communications Aware Evasion Attacks—0
Generating Semantically Valid Adversarial Questions for TableQA—0
Adversarial Attack on Hierarchical Graph Pooling Neural Networks—0
Investigating Vulnerability to Adversarial Examples on Multimodal Data Fusion in Deep Learning—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