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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
Shapley Homology: Topological Analysis of Sample Influence for Neural Networks—0
Detecting AI Trojans Using Meta Neural AnalysisCode0
Deep k-NN Defense against Clean-label Data Poisoning AttacksCode0
Maximal adversarial perturbations for obfuscation: Hiding certain attributes while preserving rest—0
FR-GAN: Fair and Robust Training—0
Certified Robustness to Adversarial Label-Flipping Attacks via Randomized Smoothing—0
Detection of Backdoors in Trained Classifiers Without Access to the Training Set—0
On Defending Against Label Flipping Attacks on Malware Detection Systems—0
Seeing is Not Believing: Camouflage Attacks on Image Scaling AlgorithmsCode0
Explaining Vulnerabilities to Adversarial Machine Learning through Visual AnalyticsCode0
Poisoning Attacks with Generative Adversarial NetsCode0
Mixed Strategy Game Model Against Data Poisoning Attacks—0
An Investigation of Data Poisoning Defenses for Online Learning—0
Data Poisoning Attacks on Stochastic Bandits—0
Robust Federated Training via Collaborative Machine Teaching using Trusted Instances—0
Data Poisoning Attack against Knowledge Graph Embedding—0
Can Machine Learning Model with Static Features be Fooled: an Adversarial Machine Learning Approach—0
Adversarial Learning in Statistical Classification: A Comprehensive Review of Defenses Against Attacks—0
Data Poisoning against Differentially-Private Learners: Attacks and Defenses—0
SLSGD: Secure and Efficient Distributed On-device Machine Learning—0
Online Data Poisoning Attack—0
TrojDRL: Trojan Attacks on Deep Reinforcement Learning AgentsCode0
Is Spiking Secure? A Comparative Study on the Security Vulnerabilities of Spiking and Deep Neural Networks—0
Spectrum Data Poisoning with Adversarial Deep Learning—0
Reaching Data Confidentiality and Model Accountability on the CalTrain—0
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