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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 326–350 of 492 papers

TitleStatusHype
Winter Soldier: Backdooring Language Models at Pre-Training with Indirect Data Poisoning—0
Wolf in Sheep's Clothing - The Downscaling Attack Against Deep Learning Applications—0
You Autocomplete Me: Poisoning Vulnerabilities in Neural Code Completion—0
Histopathological Image Classification and Vulnerability Analysis using Federated Learning—0
How Robust are Randomized Smoothing based Defenses to Data Poisoning?—0
Humpty Dumpty: Controlling Word Meanings via Corpus Poisoning—0
WW-FL: Secure and Private Large-Scale Federated Learning—0
Hyperparameter Learning under Data Poisoning: Analysis of the Influence of Regularization via Multiobjective Bilevel Optimization—0
If You Don't Understand It, Don't Use It: Eliminating Trojans with Filters Between Layers—0
Imperceptible Rhythm Backdoor Attacks: Exploring Rhythm Transformation for Embedding Undetectable Vulnerabilities on Speech Recognition—0
Adversarial Backdoor Attack by Naturalistic Data Poisoning on Trajectory Prediction in Autonomous Driving—0
Indiscriminate Data Poisoning Attacks on Pre-trained Feature Extractors—0
Influence Based Defense Against Data Poisoning Attacks in Online Learning—0
Influence Function based Data Poisoning Attacks to Top-N Recommender Systems—0
Instructions as Backdoors: Backdoor Vulnerabilities of Instruction Tuning for Large Language Models—0
Interactive System-wise Anomaly Detection—0
Inverting Gradient Attacks Makes Powerful Data Poisoning—0
Investigating cybersecurity incidents using large language models in latest-generation wireless networks—0
Invisible Backdoor Attacks Using Data Poisoning in the Frequency Domain—0
TrISec: Training Data-Unaware Imperceptible Security Attacks on Deep Neural Networks—0
Is feature selection secure against training data poisoning?—0
Just How Toxic is Data Poisoning? A Benchmark for Backdoor and Data Poisoning Attacks—0
Label Flipping Data Poisoning Attack Against Wearable Human Activity Recognition System—0
Label Sanitization against Label Flipping Poisoning Attacks—0
From Vulnerabilities to Remediation: A Systematic Literature Review of LLMs in Code Security—0
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