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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 251–300 of 492 papers

TitleStatusHype
Recursive Euclidean Distance Based Robust Aggregation Technique For Federated Learning—0
Redactor: A Data-centric and Individualized Defense Against Inference Attacks—0
FedPrompt: Communication-Efficient and Privacy Preserving Prompt Tuning in Federated Learning—0
Regularisation Can Mitigate Poisoning Attacks: A Novel Analysis Based on Multiobjective Bilevel Optimisation—0
Regularization Helps with Mitigating Poisoning Attacks: Distributionally-Robust Machine Learning Using the Wasserstein Distance—0
Regularized Robustly Reliable Learners and Instance Targeted Attacks—0
Reinforcement Learning For Data Poisoning on Graph Neural Networks—0
Releasing Malevolence from Benevolence: The Menace of Benign Data on Machine Unlearning—0
Reliable Poisoned Sample Detection against Backdoor Attacks Enhanced by Sharpness Aware Minimization—0
Reputation-Based Federated Learning Defense to Mitigate Threats in EEG Signal Classification—0
Rethinking Backdoor Data Poisoning Attacks in the Context of Semi-Supervised Learning—0
Revamping Federated Learning Security from a Defender's Perspective: A Unified Defense with Homomorphic Encrypted Data Space—0
Detection of Backdoors in Trained Classifiers Without Access to the Training Set—0
Revealing Perceptible Backdoors, without the Training Set, via the Maximum Achievable Misclassification Fraction Statistic—0
Reverse Engineering Imperceptible Backdoor Attacks on Deep Neural Networks for Detection and Training Set Cleansing—0
Review-Incorporated Model-Agnostic Profile Injection Attacks on Recommender Systems—0
Robust Federated Training via Collaborative Machine Teaching using Trusted Instances—0
Robust learning under clean-label attack—0
Robustly-reliable learners under poisoning attacks—0
Robust Variational Autoencoder for Tabular Data with Beta Divergence—0
SAFELOC: Overcoming Data Poisoning Attacks in Heterogeneous Federated Machine Learning for Indoor Localization—0
SafeNet: The Unreasonable Effectiveness of Ensembles in Private Collaborative Learning—0
Saving Stochastic Bandits from Poisoning Attacks via Limited Data Verification—0
Securing Traffic Sign Recognition Systems in Autonomous Vehicles—0
Security and Privacy Challenges in Deep Learning Models—0
Security and Privacy Challenges of Large Language Models: A Survey—0
Security Concerns for Large Language Models: A Survey—0
Security of Distributed Machine Learning: A Game-Theoretic Approach to Design Secure DSVM—0
SEEP: Training Dynamics Grounds Latent Representation Search for Mitigating Backdoor Poisoning Attacks—0
Self-Adaptive and Robust Federated Spectrum Sensing without Benign Majority for Cellular Networks—0
Shapley Homology: Topological Analysis of Sample Influence for Neural Networks—0
SHFL: Secure Hierarchical Federated Learning Framework for Edge Networks—0
Silent Branding Attack: Trigger-free Data Poisoning Attack on Text-to-Image Diffusion Models—0
Sky of Unlearning (SoUL): Rewiring Federated Machine Unlearning via Selective Pruning—0
Sniper GMMs: Structured Gaussian mixtures poison ML on large n small p data with high efficacy—0
Is Spiking Secure? A Comparative Study on the Security Vulnerabilities of Spiking and Deep Neural Networks—0
Sonic: Fast and Transferable Data Poisoning on Clustering Algorithms—0
Spectrum Data Poisoning with Adversarial Deep Learning—0
Sself: Robust Federated Learning against Stragglers and Adversaries—0
SSL-OTA: Unveiling Backdoor Threats in Self-Supervised Learning for Object Detection—0
Stealthy LLM-Driven Data Poisoning Attacks Against Embedding-Based Retrieval-Augmented Recommender Systems—0
Survey of Security and Data Attacks on Machine Unlearning In Financial and E-Commerce—0
SusFL: Energy-Aware Federated Learning-based Monitoring for Sustainable Smart Farms—0
Swallowing the Poison Pills: Insights from Vulnerability Disparity Among LLMs—0
Sybil-based Virtual Data Poisoning Attacks in Federated Learning—0
Systematic Evaluation of Backdoor Data Poisoning Attacks on Image Classifiers—0
Systematic Testing of the Data-Poisoning Robustness of KNN—0
Targeted Data Poisoning Attack on News Recommendation System by Content Perturbation—0
Targeted Data Poisoning for Black-Box Audio Datasets Ownership Verification—0
A Targeted Attack on Black-Box Neural Machine Translation with Parallel Data Poisoning—0
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