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 1201–1250 of 1808 papers

TitleStatusHype
When Side-Channel Attacks Break the Black-Box Property of Embedded Artificial Intelligence—0
Membership Inference Attacks Against Temporally Correlated Data in Deep Reinforcement Learning—0
White-Box Target Attack for EEG-Based BCI Regression Problems—0
Wicked Oddities: Selectively Poisoning for Effective Clean-Label Backdoor Attacks—0
XSub: Explanation-Driven Adversarial Attack against Blackbox Classifiers via Feature Substitution—0
Yet another but more efficient black-box adversarial attack: tiling and evolution strategies—0
You Don't Need Robust Machine Learning to Manage Adversarial Attack Risks—0
Zero-Query Transfer Attacks on Context-Aware Object Detectors—0
Zeroth-Order Stochastic Alternating Direction Method of Multipliers for Nonconvex Nonsmooth Optimization—0
ZhichunRoad at SemEval-2022 Task 2: Adversarial Training and Contrastive Learning for Multiword Representations—0
Feature-Filter: Detecting Adversarial Examples through Filtering off Recessive Features—0
Feature Importance Guided Attack: A Model Agnostic Adversarial Attack—0
Feature Unlearning for Pre-trained GANs and VAEs—0
Feature Visualization within an Automated Design Assessment leveraging Explainable Artificial Intelligence Methods—0
FedDef: Defense Against Gradient Leakage in Federated Learning-based Network Intrusion Detection Systems—0
Few-Features Attack to Fool Machine Learning Models through Mask-Based GAN—0
Learning Transferable Adversarial Robust Representations via Multi-view Consistency—0
F&F Attack: Adversarial Attack against Multiple Object Trackers by Inducing False Negatives and False Positives—0
FineFool: Fine Object Contour Attack via Attention—0
FlippedRAG: Black-Box Opinion Manipulation Adversarial Attacks to Retrieval-Augmented Generation Models—0
Fooling Adversarial Training with Inducing Noise—0
Fooling Adversarial Training with Induction Noise—0
FoolSDEdit: Deceptively Steering Your Edits Towards Targeted Attribute-aware Distribution—0
Forbidden Facts: An Investigation of Competing Objectives in Llama-2—0
Fortify Machine Learning Production Systems: Detect and Classify Adversarial Attacks—0
FRAUD-RLA: A new reinforcement learning adversarial attack against credit card fraud detection—0
Frequency-aware GAN for Adversarial Manipulation Generation—0
Frequency-Tuned Universal Adversarial Attacks—0
From Environmental Sound Representation to Robustness of 2D CNN Models Against Adversarial Attacks—0
From Sound Representation to Model Robustness—0
GAIM: Attacking Graph Neural Networks via Adversarial Influence Maximization—0
GasHis-Transformer: A Multi-scale Visual Transformer Approach for Gastric Histopathological Image Detection—0
Gender Bias and Universal Substitution Adversarial Attacks on Grammatical Error Correction Systems for Automated Assessment—0
General Adversarial Defense Against Black-box Attacks via Pixel Level and Feature Level Distribution Alignments—0
Generalization to Mitigate Synonym Substitution Attacks—0
Generating Adversarial Attacks in the Latent Space—0
Generating Adversarial Examples with an Optimized Quality—0
Generating Adversarial Inputs Using A Black-box Differential Technique—0
Generating Black-Box Adversarial Examples in Sparse Domain—0
Generating Out of Distribution Adversarial Attack using Latent Space Poisoning—0
Generating Semantic Adversarial Examples via Feature Manipulation—0
Generating Semantically Valid Adversarial Questions for TableQA—0
Generating Unrestricted Adversarial Examples via Three Parameters—0
Generating Valid and Natural Adversarial Examples with Large Language Models—0
Generating Watermarked Adversarial Texts—0
Generative Adversarial Network-Driven Detection of Adversarial Tasks in Mobile Crowdsensing—0
Generative Adversarial Patches for Physical Attacks on Cross-Modal Pedestrian Re-Identification—0
Global Robustness Verification Networks—0
Golden Ratio Search: A Low-Power Adversarial Attack for Deep Learning based Modulation Classification—0
Gradient-based adversarial attacks on categorical sequence models via traversing an embedded world—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