SOTAVerified

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 251–275 of 492 papers

TitleStatusHype
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
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