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 601–650 of 1808 papers

TitleStatusHype
LookHere: Vision Transformers with Directed Attention Generalize and ExtrapolateCode0
Trustworthy Actionable Perturbations—0
Safeguarding Vision-Language Models Against Patched Visual Prompt Injectors—0
Adversarial Robustness for Visual Grounding of Multimodal Large Language ModelsCode0
Towards Evaluating the Robustness of Automatic Speech Recognition Systems via Audio Style Transfer—0
Improving Transferable Targeted Adversarial Attack via Normalized Logit Calibration and Truncated Feature Mixing—0
Disttack: Graph Adversarial Attacks Toward Distributed GNN TrainingCode0
BB-Patch: BlackBox Adversarial Patch-Attack using Zeroth-Order Optimization—0
Untargeted Adversarial Attack on Knowledge Graph Embeddings—0
To Each (Textual Sequence) Its Own: Improving Memorized-Data Unlearning in Large Language Models—0
Probing Unlearned Diffusion Models: A Transferable Adversarial Attack PerspectiveCode0
A General Black-box Adversarial Attack on Graph-based Fake News Detectors—0
DIP-Watermark: A Double Identity Protection Method Based on Robust Adversarial Watermark—0
Beyond Score Changes: Adversarial Attack on No-Reference Image Quality Assessment from Two Perspectives—0
AED-PADA:Improving Generalizability of Adversarial Example Detection via Principal Adversarial Domain Adaptation—0
SA-Attack: Speed-adaptive stealthy adversarial attack on trajectory predictionCode0
Towards a Novel Perspective on Adversarial Examples Driven by Frequency—0
Adversarial Identity Injection for Semantic Face Image Synthesis—0
Counterfactual Explanations for Face Forgery Detection via Adversarial Removal of ArtifactsCode0
Towards Building a Robust Toxicity Predictor—0
BruSLeAttack: A Query-Efficient Score-Based Black-Box Sparse Adversarial Attack—0
Adversarial Attacks and Dimensionality in Text Classifiers—0
Patch Synthesis for Property Repair of Deep Neural Networks—0
Multi-granular Adversarial Attacks against Black-box Neural Ranking Models—0
READ: Improving Relation Extraction from an ADversarial PerspectiveCode0
Jailbreaking Prompt Attack: A Controllable Adversarial Attack against Diffusion Models—0
The Double-Edged Sword of Input Perturbations to Robust Accurate Fairness—0
Uncertainty-Aware SAR ATR: Defending Against Adversarial Attacks via Bayesian Neural Networks—0
Deep Learning for Robust and Explainable Models in Computer Vision—0
CosalPure: Learning Concept from Group Images for Robust Co-Saliency Detection—0
LinkPrompt: Natural and Universal Adversarial Attacks on Prompt-based Language ModelsCode0
Diffusion Attack: Leveraging Stable Diffusion for Naturalistic Image Attacking—0
Capsule Neural Networks as Noise Stabilizer for Time Series Data—0
FMM-Attack: A Flow-based Multi-modal Adversarial Attack on Video-based LLMsCode0
DD-RobustBench: An Adversarial Robustness Benchmark for Dataset DistillationCode0
As Firm As Their Foundations: Can open-sourced foundation models be used to create adversarial examples for downstream tasks?—0
LocalStyleFool: Regional Video Style Transfer Attack Using Segment Anything Model—0
Robust Overfitting Does Matter: Test-Time Adversarial Purification With FGSMCode0
SSCAE -- Semantic, Syntactic, and Context-aware natural language Adversarial Examples generator—0
A Modified Word Saliency-Based Adversarial Attack on Text Classification Models—0
Benchmarking Adversarial Robustness of Image Shadow Removal with Shadow-adaptive Attacks—0
Fast Inference of Removal-Based Node InfluenceCode0
Attack Deterministic Conditional Image Generative Models for Diverse and Controllable Generation—0
IOI: Invisible One-Iteration Adversarial Attack on No-Reference Image- and Video-Quality MetricsCode0
Hard-label based Small Query Black-box Adversarial AttackCode0
Prepared for the Worst: A Learning-Based Adversarial Attack for Resilience Analysis of the ICP Algorithm—0
Adversarial Infrared Geometry: Using Geometry to Perform Adversarial Attack against Infrared Pedestrian Detectors—0
SAR-AE-SFP: SAR Imagery Adversarial Example in Real Physics domain with Target Scattering Feature Parameters—0
Robust Deep Reinforcement Learning Through Adversarial Attacks and Training : A Survey—0
Unraveling Adversarial Examples against Speaker Identification -- Techniques for Attack Detection and Victim Model Classification—0
Show:102550
← PrevPage 13 of 37Next →
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