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
Exacerbating Algorithmic Bias through Fairness AttacksCode0
A Distributed Black-Box Adversarial Attack Based on Multi-Group Particle Swarm OptimizationCode0
Blindfolded Attackers Still Threatening: Strict Black-Box Adversarial Attacks on Graphs—0
Attack Agnostic Detection of Adversarial Examples via Random Subspace Analysis—0
Generating Out of Distribution Adversarial Attack using Latent Space Poisoning—0
Towards Natural Robustness Against Adversarial Examples—0
Channel Effects on Surrogate Models of Adversarial Attacks against Wireless Signal Classifiers—0
FenceBox: A Platform for Defeating Adversarial Examples with Data Augmentation TechniquesCode0
Adversarial Attacks on Deep Graph Matching—0
Contrastive Zero-Shot Learning for Cross-Domain Slot Filling with Adversarial Attack—0
Enhancing Neural Models with Vulnerability via Adversarial AttackCode0
Just One Moment: Structural Vulnerability of Deep Action Recognition against One Frame Attack—0
A Targeted Universal Attack on Graph Convolutional NetworkCode0
FaceGuard: A Self-Supervised Defense Against Adversarial Face Images—0
NaturalAE: Natural and Robust Physical Adversarial Examples for Object Detectors—0
Adversarial Attack on Facial Recognition using Visible Light—0
Probing Model Signal-Awareness via Prediction-Preserving Input Minimization—0
A Sweet Rabbit Hole by DARCY: Using Honeypots to Detect Universal Trigger's Adversarial Attacks—0
Multi-Task Adversarial Attack—0
Adversarial Profiles: Detecting Out-Distribution & Adversarial Samples in Pre-trained CNNs—0
Dynamic backdoor attacks against federated learning—0
Fooling the primate brain with minimal, targeted image manipulation—0
Efficient and Transferable Adversarial Examples from Bayesian Neural NetworksCode0
Bridging the Performance Gap between FGSM and PGD Adversarial TrainingCode0
Defense-friendly Images in Adversarial Attacks: Dataset and Metrics for Perturbation DifficultyCode0
Dynamically Sampled Nonlocal Gradients for Stronger Adversarial Attacks—0
Deep-Dup: An Adversarial Weight Duplication Attack Framework to Crush Deep Neural Network in Multi-Tenant FPGACode0
Detecting Word Sense Disambiguation Biases in Machine Translation for Model-Agnostic Adversarial AttacksCode0
Utilizing Multimodal Feature Consistency to Detect Adversarial Examples on Clinical Summaries—0
Generalization to Mitigate Synonym Substitution Attacks—0
Second-Order NLP Adversarial Examples—0
TextAttack: Lessons learned in designing Python frameworks for NLP—0
Perception Improvement for Free: Exploring Imperceptible Black-box Adversarial Attacks on Image Classification—0
Can the state of relevant neurons in a deep neural networks serve as indicators for detecting adversarial attacks?—0
Defense-guided Transferable Adversarial Attacks—0
Rewriting Meaningful Sentences via Conditional BERT Sampling and an application on fooling text classifiers—0
Learning Black-Box Attackers with Transferable Priors and Query FeedbackCode0
L-RED: Efficient Post-Training Detection of Imperceptible Backdoor Attacks without Access to the Training Set—0
A Hamiltonian Monte Carlo Method for Probabilistic Adversarial Attack and Learning—0
Reverse Engineering Imperceptible Backdoor Attacks on Deep Neural Networks for Detection and Training Set Cleansing—0
Explain2Attack: Text Adversarial Attacks via Cross-Domain InterpretabilityCode0
An Evasion Attack against Stacked Capsule AutoencoderCode0
GreedyFool: Multi-Factor Imperceptibility and Its Application to Designing a Black-box Adversarial AttackCode0
An Analysis of Robustness of Non-Lipschitz NetworksCode0
Learning to Attack with Fewer Pixels: A Probabilistic Post-hoc Framework for Refining Arbitrary Dense Adversarial Attacks—0
EFSG: Evolutionary Fooling Sentences Generator—0
Learning Task-aware Robust Deep Learning Systems—0
Adversarial Attacks to Machine Learning-Based Smart Healthcare Systems—0
Adversarial attacks on audio source separation—0
Adversarial Patch Attacks on Monocular Depth Estimation Networks—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