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 1701–1750 of 1808 papers

TitleStatusHype
Availability Adversarial Attack and Countermeasures for Deep Learning-based Load ForecastingCode0
Adversarial-Playground: A Visualization Suite Showing How Adversarial Examples Fool Deep LearningCode0
DeSparsify: Adversarial Attack Against Token Sparsification Mechanisms in Vision TransformersCode0
Adversarial Attacks on Parts of Speech: An Empirical Study in Text-to-Image GenerationCode0
Robust Overfitting Does Matter: Test-Time Adversarial Purification With FGSMCode0
Targeted Mismatch Adversarial Attack: Query with a Flower to Retrieve the TowerCode0
Delving into Transferable Adversarial Examples and Black-box AttacksCode0
A Uniform Framework for Anomaly Detection in Deep Neural NetworksCode0
Robust Reinforcement Learning under model misspecificationCode0
Enhancing Robust Representation in Adversarial Training: Alignment and Exclusion CriteriaCode0
Deflecting Adversarial Attacks with Pixel DeflectionCode0
Multi-Granularity Tibetan Textual Adversarial Attack Method Based on Masked Language ModelCode0
DANCE: Enhancing saliency maps using decoysCode0
Multi-Instance Adversarial Attack on GNN-Based Malicious Domain DetectionCode0
Towards Transferable Targeted Adversarial ExamplesCode0
TASA: Deceiving Question Answering Models by Twin Answer Sentences AttackCode0
Adversarial Attacks on Large Language Models Using Regularized RelaxationCode0
Defense-friendly Images in Adversarial Attacks: Dataset and Metrics for Perturbation DifficultyCode0
Task and Model Agnostic Adversarial Attack on Graph Neural NetworksCode0
T-BFA: Targeted Bit-Flip Adversarial Weight AttackCode0
NATTACK: Learning the Distributions of Adversarial Examples for an Improved Black-Box Attack on Deep Neural NetworksCode0
Defense against Adversarial Attacks Using High-Level Representation Guided DenoiserCode0
Defending Substitution-Based Profile Pollution Attacks on Sequential RecommendersCode0
Natural Language Adversarial Defense through Synonym EncodingCode0
Role of Spatial Context in Adversarial Robustness for Object DetectionCode0
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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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