Secure On-Device Video OOD Detection Without Backpropagation

Out-of-Distribution (OOD) detection is critical for ensuring the reliability of machine learning models in safety-critical applications such as autonomous driving and medical diagnosis. While deploying personalized OOD detection directly on edge devices is desirable, it remains challenging due to large model sizes and the computational infeasibility of on-device training. Federated learning partially addresses this but still requires gradient computation and backpropagation, exceeding the capabilities of many edge devices. To overcome these challenges, we propose SecDOOD, a secure cloud-device collaboration framework for efficient on-device OOD detection without requiring device-side backpropagation. SecDOOD utilizes cloud resources for model training while ensuring user data privacy by retaining sensitive information on-device. Central to SecDOOD is a HyperNetwork-based personalized parameter generation module, which adapts cloud-trained models to device-specific distributions by dynamically generating local weight adjustments, effectively combining central and local information without local fine-tuning. Additionally, our dynamic feature sampling and encryption strategy selectively encrypts only the most informative feature channels, largely reducing encryption overhead without compromising detection performance. Extensive experiments across multiple datasets and OOD scenarios demonstrate that SecDOOD achieves performance comparable to fully fine-tuned models, enabling secure, efficient, and personalized OOD detection on resource-limited edge devices. To enhance accessibility and reproducibility, our code is publicly available at https://github.com/Dystopians/SecDOOD.

Advanced Image Segmentation Techniques for Neural Activity Detection via C-fos Immediate Early Gene Expression

This paper investigates the application of advanced image segmentation techniques to analyze C-fos immediate early gene expression, a crucial marker for neural activity. Due to the complexity and high variability of neural circuits, accurate segmentation of C-fos images is paramount for the development of new insights into neural function. Amidst this backdrop, this research aims to improve accuracy and minimize manual intervention in C-fos image segmentation by leveraging the capabilities of CNNs and the Unet model. We describe the development of a novel workflow for the segmentation process involving Convolutional Neural Networks (CNNs) and the Unet model, demonstrating their efficiency in various image segmentation tasks. Our workflow incorporates pre-processing steps such as cropping, image feature extraction, and clustering for the training dataset selection. We used an AutoEncoder model to extract features and implement constrained clustering to identify similarities and differences in image types. Additionally, we utilized manual and automatic labeling approaches to enhance the performance of our model. We demonstrated the effectiveness of our method in distinguishing areas with significant C-fos expression from normal tissue areas. Lastly, we implemented a modified Unet network for the detection of C-fos expressions. This research contributes to the development of more efficient and automated image segmentation methods, advancing the understanding of neural function in neuroscience research.