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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 201–250 of 492 papers

TitleStatusHype
Backdoor Attack on Vision Language Models with Stealthy Semantic Manipulation—0
Backdoor Attacks Against Incremental Learners: An Empirical Evaluation Study—0
Certifiers Make Neural Networks Vulnerable to Availability Attacks—0
Backdoor Embedding in Convolutional Neural Network Models via Invisible Perturbation—0
Backdoors in DRL: Four Environments Focusing on In-distribution Triggers—0
Backdoor Vulnerabilities in Normally Trained Deep Learning Models—0
BadSampler: Harnessing the Power of Catastrophic Forgetting to Poison Byzantine-robust Federated Learning—0
BadSR: Stealthy Label Backdoor Attacks on Image Super-Resolution—0
Bait and Switch: Online Training Data Poisoning of Autonomous Driving Systems—0
FedGT: Identification of Malicious Clients in Federated Learning with Secure Aggregation—0
Beyond Boundaries: A Comprehensive Survey of Transferable Attacks on AI Systems—0
Beyond the Model: Data Pre-processing Attack to Deep Learning Models in Android Apps—0
BiCert: A Bilinear Mixed Integer Programming Formulation for Precise Certified Bounds Against Data Poisoning Attacks—0
Blockchain-based Federated Recommendation with Incentive Mechanism—0
Blockchain for Large Language Model Security and Safety: A Holistic Survey—0
Boosting Backdoor Attack with A Learnable Poisoning Sample Selection Strategy—0
BrainWash: A Poisoning Attack to Forget in Continual Learning—0
Breaking Down the Defenses: A Comparative Survey of Attacks on Large Language Models—0
Breaking Fair Binary Classification with Optimal Flipping Attacks—0
Can Machine Learning Model with Static Features be Fooled: an Adversarial Machine Learning Approach—0
Balancing Privacy, Robustness, and Efficiency in Machine Learning—0
Can't Boil This Frog: Robustness of Online-Trained Autoencoder-Based Anomaly Detectors to Adversarial Poisoning Attacks—0
Cascading Adversarial Bias from Injection to Distillation in Language Models—0
CATFL: Certificateless Authentication-based Trustworthy Federated Learning for 6G Semantic Communications—0
Certified Robustness of Nearest Neighbors against Data Poisoning and Backdoor Attacks—0
Certified Robustness to Adversarial Label-Flipping Attacks via Randomized Smoothing—0
Certified Robustness to Label-Flipping Attacks via Randomized Smoothing—0
Chameleon: Increasing Label-Only Membership Leakage with Adaptive Poisoning—0
Class Machine Unlearning for Complex Data via Concepts Inference and Data Poisoning—0
Clean Image May be Dangerous: Data Poisoning Attacks Against Deep Hashing—0
Clean Label Attacks against SLU Systems—0
CLEAR: Clean-Up Sample-Targeted Backdoor in Neural Networks—0
Collaborative Self Organizing Map with DeepNNs for Fake Task Prevention in Mobile Crowdsensing—0
Compression-Resistant Backdoor Attack against Deep Neural Networks—0
Computation and Data Efficient Backdoor Attacks—0
Concealing Backdoor Model Updates in Federated Learning by Trigger-Optimized Data Poisoning—0
Context is the Key: Backdoor Attacks for In-Context Learning with Vision Transformers—0
ControlNET: A Firewall for RAG-based LLM System—0
Concealed Data Poisoning Attacks on NLP Models—0
Preventing Unauthorized Use of Proprietary Data: Poisoning for Secure Dataset Release—0
PrivacyGAN: robust generative image privacy—0
Nightshade: Prompt-Specific Poisoning Attacks on Text-to-Image Generative Models—0
Property Inference From Poisoning—0
Protecting against simultaneous data poisoning attacks—0
Protecting Proprietary Data: Poisoning for Secure Dataset Release—0
Provably effective detection of effective data poisoning attacks—0
Provably Reliable Conformal Prediction Sets in the Presence of Data Poisoning—0
Proving Data-Poisoning Robustness in Decision Trees—0
Purifying Large Language Models by Ensembling a Small Language Model—0
QTrojan: A Circuit Backdoor Against Quantum Neural Networks—0
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