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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 226–250 of 492 papers

TitleStatusHype
The Stronger the Diffusion Model, the Easier the Backdoor: Data Poisoning to Induce Copyright Breaches Without Adjusting Finetuning Pipeline—0
Data-Dependent Stability Analysis of Adversarial Training—0
Revamping Federated Learning Security from a Defender's Perspective: A Unified Defense with Homomorphic Encrypted Data Space—0
SSL-OTA: Unveiling Backdoor Threats in Self-Supervised Learning for Object Detection—0
Adversarial Data Poisoning for Fake News Detection: How to Make a Model Misclassify a Target News without Modifying It—0
Balancing Privacy, Robustness, and Efficiency in Machine Learning—0
Progressive Poisoned Data Isolation for Training-time Backdoor DefenseCode0
TrojFSP: Trojan Insertion in Few-shot Prompt Tuning—0
Data and Model Poisoning Backdoor Attacks on Wireless Federated Learning, and the Defense Mechanisms: A Comprehensive Survey—0
Forcing Generative Models to Degenerate Ones: The Power of Data Poisoning Attacks—0
FedBayes: A Zero-Trust Federated Learning Aggregation to Defend Against Adversarial Attacks—0
Mendata: A Framework to Purify Manipulated Training Data—0
Universal Backdoor AttacksCode0
Privacy and Copyright Protection in Generative AI: A Lifecycle Perspective—0
Trainwreck: A damaging adversarial attack on image classifiersCode0
Security and Privacy Challenges in Deep Learning Models—0
Beyond Boundaries: A Comprehensive Survey of Transferable Attacks on AI Systems—0
BrainWash: A Poisoning Attack to Forget in Continual Learning—0
PACOL: Poisoning Attacks Against Continual Learners—0
RLHFPoison: Reward Poisoning Attack for Reinforcement Learning with Human Feedback in Large Language Models—0
From Trojan Horses to Castle Walls: Unveiling Bilateral Data Poisoning Effects in Diffusion ModelsCode0
Reputation-Based Federated Learning Defense to Mitigate Threats in EEG Signal Classification—0
Nightshade: Prompt-Specific Poisoning Attacks on Text-to-Image Generative Models—0
PrivacyGAN: robust generative image privacy—0
Histopathological Image Classification and Vulnerability Analysis using Federated Learning—0
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