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 1351–1400 of 1808 papers

TitleStatusHype
A black-box adversarial attack for poisoning clusteringCode0
Adversarial Attack on Large Scale GraphCode1
Adversarial attacks on deep learning models for fatty liver disease classification by modification of ultrasound image reconstruction method—0
Adversarially Robust Neural Architectures—0
Adversarial Eigen Attack on Black-Box Models—0
Point Adversarial Self Mining: A Simple Method for Facial Expression Recognition—0
SIGL: Securing Software Installations Through Deep Graph Learning—0
An Adversarial Attack Defending System for Securing In-Vehicle Networks—0
PermuteAttack: Counterfactual Explanation of Machine Learning Credit ScorecardsCode0
Near Optimal Adversarial Attacks on Stochastic Bandits and Defenses with Smoothed Responses—0
A New Perspective on Stabilizing GANs training: Direct Adversarial TrainingCode0
Accelerated Zeroth-Order and First-Order Momentum Methods from Mini to Minimax Optimization—0
Adversarial Attack and Defense Strategies for Deep Speaker Recognition SystemsCode1
Improving adversarial robustness of deep neural networks by using semantic information—0
Model Robustness with Text Classification: Semantic-preserving adversarial attacks—0
FireBERT: Hardening BERT-based classifiers against adversarial attackCode0
Adversarial Training with Fast Gradient Projection Method against Synonym Substitution based Text AttacksCode1
Visual Attack and Defense on Text—0
Robust Deep Reinforcement Learning through Adversarial LossCode1
Stabilizing Deep Tomographic Reconstruction—0
Hardware Accelerator for Adversarial Attacks on Deep Learning Neural Networks—0
Sparse Adversarial Attack via Perturbation FactorizationCode1
SemanticAdv: Generating Adversarial Examples via Attribute-conditioned Image EditingCode1
SimAug: Learning Robust Representations from Simulation for Trajectory PredictionCode1
Physical Adversarial Attack on Vehicle Detector in the Carla Simulator—0
DeepPeep: Exploiting Design Ramifications to Decipher the Architecture of Compact DNNs—0
Adversarial Robustness for Machine Learning Cyber Defenses Using Log Data—0
Derivation of Information-Theoretically Optimal Adversarial Attacks with Applications to Robust Machine Learning—0
Attacking and Defending Machine Learning Applications of Public CloudCode2
Towards Accuracy-Fairness Paradox: Adversarial Example-based Data Augmentation for Visual Debiasing—0
From Sound Representation to Model Robustness—0
Adversarial Privacy-preserving FilterCode0
T-BFA: Targeted Bit-Flip Adversarial Weight AttackCode0
Robust Tracking against Adversarial AttacksCode1
Adversarial Immunization for Certifiable Robustness on GraphsCode1
Semantic Equivalent Adversarial Data Augmentation for Visual Question AnsweringCode1
Exploiting vulnerabilities of deep neural networks for privacy protectionCode0
DDR-ID: Dual Deep Reconstruction Networks Based Image Decomposition for Anomaly Detection—0
Anomaly Detection in Unsupervised Surveillance Setting Using Ensemble of Multimodal Data with Adversarial Defense—0
Backdoor Learning: A SurveyCode2
Accelerated Stochastic Gradient-free and Projection-free MethodsCode0
AdvFlow: Inconspicuous Black-box Adversarial Attacks using Normalizing FlowsCode1
Patch-wise Attack for Fooling Deep Neural NetworkCode1
Pasadena: Perceptually Aware and Stealthy Adversarial Denoise Attack—0
Generating Adversarial Inputs Using A Black-box Differential Technique—0
Miss the Point: Targeted Adversarial Attack on Multiple Landmark DetectionCode1
Evaluation of Adversarial Training on Different Types of Neural Networks in Deep Learning-based IDSs—0
Black-box Adversarial Example Generation with Normalizing FlowsCode1
On Data Augmentation and Adversarial Risk: An Empirical Analysis—0
Adversarial Machine Learning Attacks and Defense Methods in the Cyber Security Domain—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