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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 451–475 of 492 papers

TitleStatusHype
Mitigating Backdoor Attack by Injecting Proactive Defensive BackdoorCode0
2D-OOB: Attributing Data Contribution Through Joint Valuation FrameworkCode0
TrojDRL: Trojan Attacks on Deep Reinforcement Learning AgentsCode0
Testing the Robustness of Learned Index StructuresCode0
Seeing Is Not Always Believing: Invisible Collision Attack and Defence on Pre-Trained ModelsCode0
Incompatibility Clustering as a Defense Against Backdoor Poisoning AttacksCode0
Provable Robustness of (Graph) Neural Networks Against Data Poisoning and Backdoor AttacksCode0
Mole Recruitment: Poisoning of Image Classifiers via Selective Batch SamplingCode0
Multi-Faceted Studies on Data Poisoning can Advance LLM DevelopmentCode0
Seeing is Not Believing: Camouflage Attacks on Image Scaling AlgorithmsCode0
BagFlip: A Certified Defense against Data PoisoningCode0
The Effect of Data Poisoning on Counterfactual ExplanationsCode0
Delta-Influence: Unlearning Poisons via Influence FunctionsCode0
Nonsmooth Implicit Differentiation: Deterministic and Stochastic Convergence RatesCode0
Classification Auto-Encoder based Detector against Diverse Data Poisoning AttacksCode0
Explainable Data Poison Attacks on Human Emotion Evaluation Systems based on EEG SignalsCode0
Odyssey: Creation, Analysis and Detection of Trojan ModelsCode0
Putting words into the system's mouth: A targeted attack on neural machine translation using monolingual data poisoningCode0
On Adversarial Bias and the Robustness of Fair Machine LearningCode0
Putting words into the system’s mouth: A targeted attack on neural machine translation using monolingual data poisoningCode0
The Hammer and the Nut: Is Bilevel Optimization Really Needed to Poison Linear Classifiers?Code0
Defending Regression Learners Against Poisoning AttacksCode0
Defending Distributed Classifiers Against Data Poisoning AttacksCode0
VenoMave: Targeted Poisoning Against Speech RecognitionCode0
Analysis and Detectability of Offline Data Poisoning Attacks on Linear Dynamical SystemsCode0
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