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 926–950 of 1808 papers

TitleStatusHype
Query-Free Adversarial Transfer via Undertrained Surrogates—0
Blindfolded Attackers Still Threatening: Strict Black-Box Adversarial Attacks on Graphs—0
RAF: Recursive Adversarial Attacks on Face Recognition Using Extremely Limited Queries—0
Attack Agnostic Detection of Adversarial Examples via Random Subspace Analysis—0
RAT: Adversarial Attacks on Deep Reinforcement Agents for Targeted Behaviors—0
The Best Defense is Attack: Repairing Semantics in Textual Adversarial Examples—0
Realistic Scatterer Based Adversarial Attacks on SAR Image Classifiers—0
Real-Time Robust Video Object Detection System Against Physical-World Adversarial Attacks—0
Real-time, Universal, and Robust Adversarial Attacks Against Speaker Recognition Systems—0
Real-World Adversarial Examples involving Makeup Application—0
Reasoning Chain Based Adversarial Attack for Multi-hop Question Answering—0
Text Adversarial Purification as Defense against Adversarial Attacks—0
Recent Advances in Reliable Deep Graph Learning: Inherent Noise, Distribution Shift, and Adversarial Attack—0
Towards Safer Generative Language Models: A Survey on Safety Risks, Evaluations, and Improvements—0
RecUP-FL: Reconciling Utility and Privacy in Federated Learning via User-configurable Privacy Defense—0
Redefining Machine Unlearning: A Conformal Prediction-Motivated Approach—0
Refining Adaptive Zeroth-Order Optimization at Ease—0
Region-Wise Attack: On Efficient Generation of Robust Physical Adversarial Examples—0
Reinforce Attack: Adversarial Attack against BERT with Reinforcement Learning—0
Reinforcement Learning Based Sparse Black-box Adversarial Attack on Video Recognition Models—0
ReLATE: Resilient Learner Selection for Multivariate Time-Series Classification Against Adversarial Attacks—0
Replace-then-Perturb: Targeted Adversarial Attacks With Visual Reasoning for Vision-Language Models—0
Residue-Based Natural Language Adversarial Attack Detection—0
Resilient and constrained consensus against adversarial attacks: A distributed MPC framework—0
Resilient Dynamic Average Consensus based on Trusted agents—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