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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 1–50 of 492 papers

TitleStatusHype
A Survey of Attacks on Large Vision-Language Models: Resources, Advances, and Future TrendsCode4
Safety at Scale: A Comprehensive Survey of Large Model SafetyCode3
BackdoorLLM: A Comprehensive Benchmark for Backdoor Attacks and Defenses on Large Language ModelsCode3
Data Poisoning in LLMs: Jailbreak-Tuning and Scaling LawsCode3
Quantifying the robustness of deep multispectral segmentation models against natural perturbations and data poisoningCode3
SoK: Benchmarking Poisoning Attacks and Defenses in Federated LearningCode2
Watch Out for Your Agents! Investigating Backdoor Threats to LLM-Based AgentsCode2
Shadowcast: Stealthy Data Poisoning Attacks Against Vision-Language ModelsCode2
Backdoor Learning: A SurveyCode2
VLMs Can Aggregate Scattered Training PatchesCode1
Data Poisoning in Deep Learning: A SurveyCode1
BackdoorMBTI: A Backdoor Learning Multimodal Benchmark Tool Kit for Backdoor Defense EvaluationCode1
PoisonBench: Assessing Large Language Model Vulnerability to Data PoisoningCode1
Fast-FedUL: A Training-Free Federated Unlearning with Provable Skew ResilienceCode1
PureGen: Universal Data Purification for Train-Time Poison Defense via Generative Model DynamicsCode1
PureEBM: Universal Poison Purification via Mid-Run Dynamics of Energy-Based ModelsCode1
Optimistic Verifiable Training by Controlling Hardware NondeterminismCode1
Learning to Poison Large Language Models for Downstream ManipulationCode1
Data Poisoning based Backdoor Attacks to Contrastive LearningCode1
FlowMur: A Stealthy and Practical Audio Backdoor Attack with Limited KnowledgeCode1
IMMA: Immunizing text-to-image Models against Malicious AdaptationCode1
Vulnerabilities in AI Code Generators: Exploring Targeted Data Poisoning AttacksCode1
FedDefender: Backdoor Attack Defense in Federated LearningCode1
On the Exploitability of Instruction TuningCode1
DeepfakeArt Challenge: A Benchmark Dataset for Generative AI Art Forgery and Data Poisoning DetectionCode1
Text-to-Image Diffusion Models can be Easily Backdoored through Multimodal Data PoisoningCode1
Defending Against Patch-based Backdoor Attacks on Self-Supervised LearningCode1
Learning the Unlearnable: Adversarial Augmentations Suppress Unlearnable Example AttacksCode1
Robust Contrastive Language-Image Pre-training against Data Poisoning and Backdoor AttacksCode1
CleanCLIP: Mitigating Data Poisoning Attacks in Multimodal Contrastive LearningCode1
Poisoning Web-Scale Training Datasets is PracticalCode1
TrojanPuzzle: Covertly Poisoning Code-Suggestion ModelsCode1
Silent Killer: A Stealthy, Clean-Label, Black-Box Backdoor AttackCode1
Unlearnable Clusters: Towards Label-agnostic Unlearnable ExamplesCode1
Hidden Poison: Machine Unlearning Enables Camouflaged Poisoning AttacksCode1
CorruptEncoder: Data Poisoning based Backdoor Attacks to Contrastive LearningCode1
Backdoor Attacks for Remote Sensing Data with Wavelet TransformCode1
Generative Poisoning Using Random DiscriminatorsCode1
Amplifying Membership Exposure via Data PoisoningCode1
Not All Poisons are Created Equal: Robust Training against Data PoisoningCode1
How to Sift Out a Clean Data Subset in the Presence of Data Poisoning?Code1
Adversarial Robustness of Representation Learning for Knowledge GraphsCode1
Data Poisoning Attacks Against Multimodal EncodersCode1
Friendly Noise against Adversarial Noise: A Powerful Defense against Data Poisoning AttacksCode1
Backdoor Attacks on Crowd CountingCode1
Robustness Evaluation of Deep Unsupervised Learning Algorithms for Intrusion Detection SystemsCode1
Autoregressive Perturbations for Data PoisoningCode1
Indiscriminate Poisoning Attacks on Unsupervised Contrastive LearningCode1
Bilevel Optimization with a Lower-level Contraction: Optimal Sample Complexity without Warm-startCode1
BEAS: Blockchain Enabled Asynchronous & Secure Federated Machine LearningCode1
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