A retrospective analysis of paraquat and diquat poisoning: a single-center experience [PDF]
Frontiers in MedicineIntroductionParaquat (PQ) and diquat (DQ) are highly toxic bipyridyl herbicides, but their dominant organ injury patterns and clinical outcomes are not identical.
Yuquan Chen +5 more
doaj +2 more sources
Poisoning Web-Scale Training Datasets is Practical [PDF]
IEEE Symposium on Security and Privacy, 2023 Deep learning models are often trained on distributed, web-scale datasets crawled from the internet. In this paper, we introduce two new dataset poisoning attacks that intentionally introduce malicious examples to a model’s performance.
Nicholas Carlini +8 more
semanticscholar +1 more source
Poisoning Language Models During Instruction Tuning [PDF]
International Conference on Machine Learning, 2023 Instruction-tuned LMs such as ChatGPT, FLAN, and InstructGPT are finetuned on datasets that contain user-submitted examples, e.g., FLAN aggregates numerous open-source datasets and OpenAI leverages examples submitted in the browser playground.
Alexander Wan +3 more
semanticscholar +1 more source
FLDetector: Defending Federated Learning Against Model Poisoning Attacks via Detecting Malicious Clients [PDF]
Knowledge Discovery and Data Mining, 2022 Federated learning (FL) is vulnerable to model poisoning attacks, in which malicious clients corrupt the global model via sending manipulated model updates to the server.
Zaixi Zhang +3 more
semanticscholar +1 more source
Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learning [PDF]
IEEE Symposium on Security and Privacy, 2021 While recent works have indicated that federated learning (FL) may be vulnerable to poisoning attacks by compromised clients, their real impact on production FL systems is not fully understood.
Virat Shejwalkar +3 more
semanticscholar +1 more source
Nightshade: Prompt-Specific Poisoning Attacks on Text-to-Image Generative Models [PDF]
IEEE Symposium on Security and Privacy, 2023 Trained on billions of images, diffusion-based text-to-image models seem impervious to traditional data poisoning attacks, which typically require poison samples approaching 20% of the training set.
Shawn Shan +4 more
semanticscholar +1 more source
Wild Patterns Reloaded: A Survey of Machine Learning Security against Training Data Poisoning [PDF]
ACM Computing Surveys, 2022 The success of machine learning is fueled by the increasing availability of computing power and large training datasets. The training data is used to learn new models or update existing ones, assuming that it is sufficiently representative of the data ...
A. E. Cinà +9 more
semanticscholar +1 more source
Manipulating the Byzantine: Optimizing Model Poisoning Attacks and Defenses for Federated Learning
Network and Distributed System Security Symposium, 2021 —Federated learning (FL) enables many data owners (e.g., mobile devices) to train a joint ML model (e.g., a next-word prediction classifier) without the need of sharing their private training data.
Virat Shejwalkar, Amir Houmansadr
semanticscholar +1 more source
Backdoor Attacks on Pre-trained Models by Layerwise Weight Poisoning [PDF]
Conference on Empirical Methods in Natural Language Processing, 2021 Pre-Trained Models have been widely applied and recently proved vulnerable under backdoor attacks: the released pre-trained weights can be maliciously poisoned with certain triggers. When the triggers are activated, even the fine-tuned model will predict
Linyang Li +5 more
semanticscholar +1 more source
Weight Poisoning Attacks on Pretrained Models [PDF]
Annual Meeting of the Association for Computational Linguistics, 2020 Recently, NLP has seen a surge in the usage of large pre-trained models. Users download weights of models pre-trained on large datasets, then fine-tune the weights on a task of their choice.
Keita Kurita, Paul Michel, Graham Neubig
semanticscholar +1 more source