Food authenticity is a significant concern in the meat industry, demanding effective detection methods.This study explores the use of multispectral imaging (MSI) and deep learning for meat adulteration detection.We evaluate different deep learning models using transfer learning and preprocessing techniques in a multi-level adulteration classification task.In addition, we propose a novel approach called one-band mixed augmentation for band selection in MSI data, which outperforms traditional reflectance-based feature selection and enhances model robustness.Furthermore, employing the ninecrop approach for dataset augmentation improved the accuracy from 0.63 to 0.74 for DenseNet201 model without transfer learning.This research contributes to advancing food safety assessment practices and provides insights into the application of deep learning for preventing food adulteration.The proposed one-band mixed augmentation approach offers a novel strategy for handling band selection challenges in MSI data analysis.

In this work, we consider a scenario that includes an individual sharing his genomic data (or results obtained from his genomic data) with a service provider. In this scenario, (i) the service provider wants to make sure that received genomic data (or results) in fact belongs to the corresponding individual (and computed correctly), (ii) the individual wants to provide a digital consent along with his data specifying whether the service provider is allowed to further share his data, and (iii) if his data is shared without his consent, the individual wants to determine the service provider that is responsible for this leakage. We propose two schemes based on homomorphic signature and aggregate signature that links the information about the legitimacy of the data to the consent and the phenotype of the individual. Thus, to verify the data, each party also needs to use the correct consent and phenotype of the individual who owns the data.