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

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Showing 201–250 of 1808 papers

TitleStatusHype
Attack as the Best Defense: Nullifying Image-to-image Translation GANs via Limit-aware Adversarial AttackCode1
Iron Sharpens Iron: Defending Against Attacks in Machine-Generated Text Detection with Adversarial TrainingCode1
Adversarial Attack on Large Scale GraphCode1
Learning Safety Constraints for Large Language ModelsCode1
Amplitude-Phase Recombination: Rethinking Robustness of Convolutional Neural Networks in Frequency DomainCode1
Local Gradients Smoothing: Defense against localized adversarial attacksCode1
GenoArmory: A Unified Evaluation Framework for Adversarial Attacks on Genomic Foundation ModelsCode1
Meta Gradient Adversarial AttackCode1
Adversarial Ranking Attack and DefenseCode1
Mind the Style of Text! Adversarial and Backdoor Attacks Based on Text Style TransferCode1
Adversarial Attack On Yolov5 For Traffic And Road Sign DetectionCode1
An Adaptive Model Ensemble Adversarial Attack for Boosting Adversarial TransferabilityCode1
Adversarial Robustness Comparison of Vision Transformer and MLP-Mixer to CNNsCode1
Multi-attacks: Many images + the same adversarial attack many target labelsCode1
Anti-Adversarially Manipulated Attributions for Weakly and Semi-Supervised Semantic SegmentationCode1
High Frequency Component Helps Explain the Generalization of Convolutional Neural NetworksCode1
NeuroUnlock: Unlocking the Architecture of Obfuscated Deep Neural NetworksCode1
Object Hider: Adversarial Patch Attack Against Object DetectorsCode1
CausalAdv: Adversarial Robustness through the Lens of CausalityCode1
A Perturbation-Constrained Adversarial Attack for Evaluating the Robustness of Optical FlowCode1
Adversarial Attacks against Windows PE Malware Detection: A Survey of the State-of-the-ArtCode1
On Fast Adversarial Robustness Adaptation in Model-Agnostic Meta-LearningCode1
An Efficient Adversarial Attack for Tree EnsemblesCode1
Adversarial Self-Supervised Contrastive LearningCode1
OpenAttack: An Open-source Textual Adversarial Attack ToolkitCode1
Order-Disorder: Imitation Adversarial Attacks for Black-box Neural Ranking ModelsCode1
An integrated Auto Encoder-Block Switching defense approach to prevent adversarial attacksCode1
Adversarial Training for Free!Code1
Patch-wise++ Perturbation for Adversarial Targeted AttacksCode1
Perception Matters: Exploring Imperceptible and Transferable Anti-forensics for GAN-generated Fake Face Imagery DetectionCode1
Adversarial Vulnerabilities in Large Language Models for Time Series ForecastingCode1
Adversarial Vulnerability of Randomized EnsemblesCode1
AdvDiff: Generating Unrestricted Adversarial Examples using Diffusion ModelsCode1
An Orthogonal Classifier for Improving the Adversarial Robustness of Neural NetworksCode1
A Pilot Study of Query-Free Adversarial Attack against Stable DiffusionCode1
Physics-Based Adversarial Attack on Near-Infrared Human Detector for Nighttime Surveillance Camera SystemsCode1
Adversarial Attacks and Detection in Visual Place Recognition for Safer Robot NavigationCode1
AdvFlow: Inconspicuous Black-box Adversarial Attacks using Normalizing FlowsCode1
Proximal Splitting Adversarial Attack for Semantic SegmentationCode1
Proximal Splitting Adversarial Attacks for Semantic SegmentationCode1
Random Walks for Adversarial MeshesCode1
Rate Coding or Direct Coding: Which One is Better for Accurate, Robust, and Energy-efficient Spiking Neural Networks?Code1
Adv-Makeup: A New Imperceptible and Transferable Attack on Face RecognitionCode1
Recipe2Vec: Multi-modal Recipe Representation Learning with Graph Neural NetworksCode1
Rethinking Image Restoration for Object DetectionCode1
Revealing Vulnerabilities in Stable Diffusion via Targeted AttacksCode1
T3: Tree-Autoencoder Constrained Adversarial Text Generation for Targeted AttackCode1
Robust Mid-Pass Filtering Graph Convolutional NetworksCode1
Robustness of on-device Models: Adversarial Attack to Deep Learning Models on Android AppsCode1
BayesOpt Adversarial AttackCode1
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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