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 1076–1100 of 1808 papers

TitleStatusHype
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