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Data Poisoning

Data Poisoning is an adversarial attack that tries to manipulate the training dataset in order to control the prediction behavior of a trained model such that the model will label malicious examples into a desired classes (e.g., labeling spam e-mails as safe).

Source: Explaining Vulnerabilities to Adversarial Machine Learning through Visual Analytics

Papers

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Showing 201–250 of 492 papers

TitleStatusHype
Analyzing the vulnerabilities in SplitFed Learning: Assessing the robustness against Data Poisoning Attacks—0
What Distributions are Robust to Indiscriminate Poisoning Attacks for Linear Learners?—0
FedDefender: Backdoor Attack Defense in Federated LearningCode1
On the Exploitability of Instruction TuningCode1
On Practical Aspects of Aggregation Defenses against Data Poisoning Attacks—0
Adversarial Backdoor Attack by Naturalistic Data Poisoning on Trajectory Prediction in Autonomous Driving—0
OVLA: Neural Network Ownership Verification using Latent Watermarks—0
Data Poisoning to Fake a Nash Equilibrium in Markov Games—0
FheFL: Fully Homomorphic Encryption Friendly Privacy-Preserving Federated Learning with Byzantine Users—0
Hyperparameter Learning under Data Poisoning: Analysis of the Influence of Regularization via Multiobjective Bilevel Optimization—0
DeepfakeArt Challenge: A Benchmark Dataset for Generative AI Art Forgery and Data Poisoning DetectionCode1
Adversarial Clean Label Backdoor Attacks and Defenses on Text Classification Systems—0
Backdoor Attacks Against Incremental Learners: An Empirical Evaluation Study—0
From Shortcuts to Triggers: Backdoor Defense with Denoised PoECode0
Instructions as Backdoors: Backdoor Vulnerabilities of Instruction Tuning for Large Language Models—0
Differentially-Private Decision Trees and Provable Robustness to Data PoisoningCode0
Faithful and Efficient Explanations for Neural Networks via Neural Tangent Kernel Surrogate ModelsCode0
Quantifying the robustness of deep multispectral segmentation models against natural perturbations and data poisoningCode3
FedGT: Identification of Malicious Clients in Federated Learning with Secure Aggregation—0
Evaluating Impact of User-Cluster Targeted Attacks in Matrix Factorisation Recommenders—0
Pick your Poison: Undetectability versus Robustness in Data Poisoning Attacks—0
Text-to-Image Diffusion Models can be Easily Backdoored through Multimodal Data PoisoningCode1
Beyond the Model: Data Pre-processing Attack to Deep Learning Models in Android Apps—0
INK: Inheritable Natural Backdoor Attack Against Model Distillation—0
Interactive System-wise Anomaly Detection—0
Defending Against Patch-based Backdoor Attacks on Self-Supervised LearningCode1
Mole Recruitment: Poisoning of Image Classifiers via Selective Batch SamplingCode0
Denoising Autoencoder-based Defensive Distillation as an Adversarial Robustness Algorithm—0
Learning the Unlearnable: Adversarial Augmentations Suppress Unlearnable Example AttacksCode1
PORE: Provably Robust Recommender Systems against Data Poisoning AttacksCode0
Recursive Euclidean Distance Based Robust Aggregation Technique For Federated Learning—0
Robust Contrastive Language-Image Pre-training against Data Poisoning and Backdoor AttacksCode1
Naive Bayes Classifiers over Missing Data: Decision and PoisoningCode0
Exploring the Limits of Model-Targeted Indiscriminate Data Poisoning AttacksCode0
CleanCLIP: Mitigating Data Poisoning Attacks in Multimodal Contrastive LearningCode1
WW-FL: Secure and Private Large-Scale Federated Learning—0
Poisoning Web-Scale Training Datasets is PracticalCode1
QTrojan: A Circuit Backdoor Against Quantum Neural Networks—0
Explainable Label-flipping Attacks on Human Emotion Assessment System—0
Data Poisoning Attacks on EEG Signal-based Risk Assessment Systems—0
Training-free Lexical Backdoor Attacks on Language ModelsCode0
Temporal Robustness against Data Poisoning—0
Run-Off Election: Improved Provable Defense against Data Poisoning AttacksCode0
CATFL: Certificateless Authentication-based Trustworthy Federated Learning for 6G Semantic Communications—0
Face Recognition in the age of CLIP & Billion image datasets—0
Explainable Data Poison Attacks on Human Emotion Evaluation Systems based on EEG SignalsCode0
Federated Transfer-Ordered-Personalized Learning for Driver Monitoring Application—0
TrojanPuzzle: Covertly Poisoning Code-Suggestion ModelsCode1
Silent Killer: A Stealthy, Clean-Label, Black-Box Backdoor AttackCode1
Computation and Data Efficient Backdoor Attacks—0
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