Data Poisoning
123 papers with code • 0 benchmarks • 0 datasets
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
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Libraries
Use these libraries to find Data Poisoning models and implementationsLatest papers with no code
Data Poisoning Attacks on Off-Policy Policy Evaluation Methods
Off-policy Evaluation (OPE) methods are a crucial tool for evaluating policies in high-stakes domains such as healthcare, where exploration is often infeasible, unethical, or expensive.
Precision Guided Approach to Mitigate Data Poisoning Attacks in Federated Learning
Federated Learning (FL) is a collaborative learning paradigm enabling participants to collectively train a shared machine learning model while preserving the privacy of their sensitive data.
Two Heads are Better than One: Nested PoE for Robust Defense Against Multi-Backdoors
In this paper, we propose Nested Product of Experts(NPoE) defense framework, which involves a mixture of experts (MoE) as a trigger-only ensemble within the PoE defense framework to simultaneously defend against multiple trigger types.
A Backdoor Approach with Inverted Labels Using Dirty Label-Flipping Attacks
Another type of data poisoning attack that is extremely relevant to our investigation is label flipping, in which the attacker manipulates the labels for a subset of data.
Have You Poisoned My Data? Defending Neural Networks against Data Poisoning
We thoroughly evaluate our proposed approach and compare it to existing state-of-the-art defenses using multiple architectures, datasets, and poison budgets.
Nonsmooth Implicit Differentiation: Deterministic and Stochastic Convergence Rates
We study the problem of efficiently computing the derivative of the fixed-point of a parametric nondifferentiable contraction map.
Optimistic Verifiable Training by Controlling Hardware Nondeterminism
The increasing compute demands of AI systems has led to the emergence of services that train models on behalf of clients lacking necessary resources.
Poisoning Programs by Un-Repairing Code: Security Concerns of AI-generated Code
However, since these large language models are trained on massive volumes of data collected from unreliable online sources (e. g., GitHub, Hugging Face), AI models become an easy target for data poisoning attacks, in which an attacker corrupts the training data by injecting a small amount of poison into it, i. e., astutely crafted malicious samples.
Don't Forget What I did?: Assessing Client Contributions in Federated Learning
Additionally, to assess client contribution under limited computational budget, we propose a scheduling procedure that considers a two-sided fairness criteria to perform expensive Shapley value computation only in a subset of training epochs.
Breaking Down the Defenses: A Comparative Survey of Attacks on Large Language Models
Large Language Models (LLMs) have become a cornerstone in the field of Natural Language Processing (NLP), offering transformative capabilities in understanding and generating human-like text.
