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 1051–1100 of 1808 papers

TitleStatusHype
Sparse Adversarial Attack in Multi-agent Reinforcement Learning—0
Sparse and Transferable Universal Singular Vectors Attack—0
SSCAE: A Novel Semantic, Syntactic, and Context-Aware Natural Language Adversarial Example Generator—0
SSCAE -- Semantic, Syntactic, and Context-aware natural language Adversarial Examples generator—0
SSMI: How to Make Objects of Interest Disappear without Accessing Object Detectors?—0
STA: Adversarial Attacks on Siamese Trackers—0
STAA-Net: A Sparse and Transferable Adversarial Attack for Speech Emotion Recognition—0
Stabilized Medical Attacks—0
Stabilizing Deep Tomographic Reconstruction—0
Standard detectors aren't (currently) fooled by physical adversarial stop signs—0
State-Aware Perturbation Optimization for Robust Deep Reinforcement Learning—0
State-of-the-art optical-based physical adversarial attacks for deep learning computer vision systems—0
Stealthy and Efficient Adversarial Attacks against Deep Reinforcement Learning—0
Stochastic Combinatorial Ensembles for Defending Against Adversarial Examples—0
Stochastic-HMDs: Adversarial Resilient Hardware Malware Detectors through Voltage Over-scaling—0
Stochastic Variance Reduced Ensemble Adversarial Attack—0
Strategically-timed State-Observation Attacks on Deep Reinforcement Learning Agents—0
Strong Black-box Adversarial Attacks on Unsupervised Machine Learning Models—0
Superclass Adversarial Attack—0
SurvAttack: Black-Box Attack On Survival Models through Ontology-Informed EHR Perturbation—0
Survey of Vulnerabilities in Large Language Models Revealed by Adversarial Attacks—0
Survey on Federated Learning Threats: concepts, taxonomy on attacks and defences, experimental study and challenges—0
Suspicion-Free Adversarial Attacks on Clustering Algorithms—0
Graded Suspiciousness of Adversarial Texts to Human—0
SWE2: SubWord Enriched and Significant Word Emphasized Framework for Hate Speech Detection—0
Symmetric Saliency-based Adversarial Attack To Speaker Identification—0
Tactics of Adversarial Attack on Deep Reinforcement Learning Agents—0
Take Fake as Real: Realistic-like Robust Black-box Adversarial Attack to Evade AIGC Detection—0
Targeted Adversarial Attacks against Neural Network Trajectory Predictors—0
Effective Targeted Attacks for Adversarial Self-Supervised Learning—0
Targeted Attacks on Timeseries Forecasting—0
Target Model Agnostic Adversarial Attacks with Query Budgets on Language Understanding Models—0
TASA: Twin Answer Sentences Attack for Adversarial Context Generation in Question Answering—0
TEAM: Temporal Adversarial Examples Attack Model against Network Intrusion Detection System Applied to RNN—0
Temporal Sparse Adversarial Attack on Sequence-based Gait Recognition—0
TenAd: A Tensor-based Low-rank Black Box Adversarial Attack for Video Classification—0
TESSER: Transfer-Enhancing Adversarial Attacks from Vision Transformers via Spectral and Semantic Regularization—0
TETRIS: Towards Exploring the Robustness of Interactive Segmentation—0
TextAttack: Lessons learned in designing Python frameworks for NLP—0
TextDefense: Adversarial Text Detection based on Word Importance Entropy—0
TextFlint: Unified Multilingual Robustness Evaluation Toolkit for Natural Language Processing—0
Agents Under Siege: Breaking Pragmatic Multi-Agent LLM Systems with Optimized Prompt Attacks—0
TextShield: Beyond Successfully Detecting Adversarial Sentences in Text Classification—0
TF-Attack: Transferable and Fast Adversarial Attacks on Large Language Models—0
The Attack Generator: A Systematic Approach Towards Constructing Adversarial Attacks—0
The Dark Side of Dynamic Routing Neural Networks: Towards Efficiency Backdoor Injection—0
The Double-Edged Sword of Input Perturbations to Robust Accurate Fairness—0
THE EFFECT OF ADVERSARIAL TRAINING: A THEORETICAL CHARACTERIZATION—0
The Effect of Similarity Measures on Accurate Stability Estimates for Local Surrogate Models in Text-based Explainable AI—0
The Efficacy of SHIELD under Different Threat Models—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