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

Showing 17261750 of 1808 papers

TitleStatusHype
Robust Text Classification: Analyzing Prototype-Based NetworksCode0
Defending Pre-trained Language Models from Adversarial Word Substitutions Without Performance SacrificeCode0
Network transferability of adversarial patches in real-time object detectionCode0
Neural Fingerprints for Adversarial Attack DetectionCode0
An Improved Genetic Algorithm and Its Application in Neural Network Adversarial AttackCode0
Attention Masks Help Adversarial Attacks to Bypass Safety DetectorsCode0
Defending against Whitebox Adversarial Attacks via Randomized DiscretizationCode0
New Adversarial Image Detection Based on Sentiment AnalysisCode0
NMT-Obfuscator Attack: Ignore a sentence in translation with only one wordCode0
A Distributed Black-Box Adversarial Attack Based on Multi-Group Particle Swarm OptimizationCode0
Noise-based cyberattacks generating fake P300 waves in brain–computer interfacesCode0
Technical Report on the CleverHans v2.1.0 Adversarial Examples LibraryCode0
Let the Noise Speak: Harnessing Noise for a Unified Defense Against Adversarial and Backdoor AttacksCode0
Temporal Consistency Constrained Transferable Adversarial Attacks with Background Mixup for Action RecognitionCode0
NOMARO: Defending against Adversarial Attacks by NOMA-Inspired Reconstruction OperationCode0
When Measures are Unreliable: Imperceptible Adversarial Perturbations toward Top-k Multi-Label LearningCode0
ADef: an Iterative Algorithm to Construct Adversarial DeformationsCode0
Attack Transferability Characterization for Adversarially Robust Multi-label ClassificationCode0
Adaptive Image Transformations for Transfer-based Adversarial AttackCode0
How many perturbations break this model? Evaluating robustness beyond adversarial accuracyCode0
SA-Attack: Speed-adaptive stealthy adversarial attack on trajectory predictionCode0
SADA: Semantic Adversarial Diagnostic Attacks for Autonomous ApplicationsCode0
TrackPGD: Efficient Adversarial Attack using Object Binary Masks against Robust Transformer TrackersCode0
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial ExamplesCode0
Attack-agnostic Adversarial Detection on Medical Data Using Explainable Machine LearningCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ResNet20Test Accuracy89.9589.95(1)Community Verified
2Xu et al.Attack: PGD2078.68Unverified
33-ensemble of multi-resolution self-ensemblesAttack: AutoAttack78.13Unverified
4TRADES-ANCRA/ResNet18Attack: AutoAttack59.7Unverified
5AdvTraining [madry2018]Attack: PGD2048.44Unverified
6TRADES [zhang2019b]Attack: PGD2045.9Unverified
7XU-NetRobust Accuracy1Unverified
#ModelMetricClaimedVerifiedStatus
13-ensemble of multi-resolution self-ensemblesAttack: AutoAttack51.28Unverified
2multi-resolution self-ensemblesAttack: AutoAttack47.85Unverified