Backdoor attacks have emerged as a primary threat to (pre-)training and deployment of deep neural networks (DNNs). While backdoor attacks have been extensively studied in a body of works, most of them were focused on single-trigger attacks that poison a dataset using a single type of trigger. Arguably, real-world backdoor attacks can be much more complex, e.g., the existence of multiple adversaries for the same dataset if it is of high value. In this work, we investigate the practical threat of backdoor attacks under the setting of \textbf{multi-trigger attacks} where multiple adversaries leverage different types of triggers to poison the same dataset. By proposing and investigating three types of multi-trigger attacks, including parallel, sequential, and hybrid attacks, we provide a set of important understandings of the coexisting, overwriting, and cross-activating effects between different triggers on the same dataset. Moreover, we show that single-trigger attacks tend to cause overly optimistic views of the security of current defense techniques, as all examined defense methods struggle to defend against multi-trigger attacks. Finally, we create a multi-trigger backdoor poisoning dataset to help future evaluation of backdoor attacks and defenses. Although our work is purely empirical, we hope it can help steer backdoor research toward more realistic settings.

Current deep learning algorithms designed for automatic ECG analysis have exhibited notable accuracy. However, akin to traditional electrocardiography, they tend to be narrowly focused and typically address a singular diagnostic condition. In this study, we specifically demonstrate the capability of a single model to predict a diverse range of both cardiac and non-cardiac discharge diagnoses based on a sole ECG collected in the emergency department. Among the 1,076 hierarchically structured ICD codes considered, our model achieves an AUROC exceeding 0.8 in 439 of them. This underscores the models proficiency in handling a wide array of diagnostic scenarios. We emphasize the potential of utilizing this model as a screening tool, potentially integrated into a holistic clinical decision support system for efficiently triaging patients in the emergency department. This research underscores the remarkable capabilities of comprehensive ECG analysis algorithms and the extensive range of possibilities facilitated by the open MIMIC-IV-ECG dataset. Finally, our data may play a pivotal role in revolutionizing the way ECG analysis is performed, marking a significant advancement in the field.