arXiv Machine Learning
Are Targeted Data Poisoning Attacks as Effective as We Think?
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Two Sides of the Same Coin: Learning the Backdoor to Remove the Backdoor
The community has recently developed various training-time defenses to counter neural backdoors introduced through data poisoning. In light of the observation that a model learns poisonous samples responsible for the backdoor easier than benign samples, these approaches either use a fixed threshold of the training loss for splitting or iteratively learn a reference model as an oracle for identifying benign samples.
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Two Sides of the Same Coin: Learning the Backdoor to Remove the Backdoor
arXiv:2607. 05748v1 Announce Type: new Abstract: The community has recently developed various training-time defenses to counter neural backdoors introduced through data poisoning.
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Pick Your Poison: Learning to Select Poison Sets for Stronger LLM Backdoor Attacks
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Empirical Evaluation of Data Poisoning Attacks in Supervised Learning
The paper evaluates two training‑time data poisoning attacks—label flipping and backdoor poisoning—on MNIST and Fashion‑MNIST using Logistic Regression, Linear SVM, and Random Forest classifiers. Label flipping degrades performance most for Logistic Regression and Linear SVM, while Random Forest remains relatively stable. Backdoor poisoning achieves near‑perfect attack success rates across all models while largely preserving clean‑test accuracy, highlighting the stealthy nature of targeted backdoors.
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Phantom Transfer: Data Poisoning can Survive Data-Level Defences
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Diversity Matters: Distributional Feature Coverage Sample Selection for Data-Efficient Backdoor Attacks
Backdoor attacks compromise training data so that a model retains clean accuracy but predicts an attacker-chosen target on triggered inputs. At very low poisoning rates, only a few samples convey the trigger--target association, making poison-sample selection critical.