Deep Cross-Modal Steganography Using Neural Representations

Steganography is the process of embedding secret data into another message or data, in such a way that it is not easily noticeable. With the advancement of deep learning, Deep Neural Networks (DNNs) have recently been utilized in steganography. However, existing deep steganography techniques are limited in scope, as they focus on specific data types and are not effective for cross-modal steganography. Therefore, We propose a deep cross-modal steganography framework using Implicit Neural Representations (INRs) to hide secret data of various formats in cover images. The proposed framework employs INRs to represent the secret data, which can handle data of various modalities and resolutions. Experiments on various secret datasets of diverse types demonstrate that the proposed approach is expandable and capable of accommodating different modalities.Comment: ICIP 202

Expanding Expressiveness of Diffusion Models with Limited Data via Self-Distillation based Fine-Tuning

Training diffusion models on limited datasets poses challenges in terms of limited generation capacity and expressiveness, leading to unsatisfactory results in various downstream tasks utilizing pretrained diffusion models, such as domain translation and text-guided image manipulation. In this paper, we propose Self-Distillation for Fine-Tuning diffusion models (SDFT), a methodology to address these challenges by leveraging diverse features from diffusion models pretrained on large source datasets. SDFT distills more general features (shape, colors, etc.) and less domain-specific features (texture, fine details, etc) from the source model, allowing successful knowledge transfer without disturbing the training process on target datasets. The proposed method is not constrained by the specific architecture of the model and thus can be generally adopted to existing frameworks. Experimental results demonstrate that SDFT enhances the expressiveness of the diffusion model with limited datasets, resulting in improved generation capabilities across various downstream tasks.Comment: WACV 202

Methods for determining bioavailability and bioaccessibility of bioactive compounds and nutrients

The health advantages of bioactive compounds and nutrients are based on intake levels and the amount of these compounds that become bioavailable and bioaccessible. The determination of bioactive compounds directly in food is not sufficient to evaluate the bioavailability and bioaccessibility and consequently their effects in vivo, since the compounds reaching the blood system result from a complex digestion process. Bioavailability is assessed by in vivo methodologies using gastrointestinal digestion, absorption, metabolism, tissue distribution, and bioactivity. In vitro methodologies allow for determining the bioaccessibility of the bioactive compounds and nutrients through liberation from the food matrix, simulation of gastrointestinal digestion, and assimilation by intestinal epithelium. This chapter presents the similarities and differences between the distinct techniques used to quantify bioavailability and bioaccessibility, as well as a brief description of the methodologies applied to determine different bioactive compounds and nutrients.The first author acknowledges the financial support from Fundação para a Ciência e a Tecnologia (FCT), Portugal, through doctoral fellowship (SFRH/BD/109124/2015). This work was supported by the national funding of FCT, and where applicable co-financed by the FEDER, within the PT2020 Partnership Agreement, under the scope of the strategic funding to the research units LEAF (UID/AGR/04129/2013), QOPNA (UID/QUI/00062/2013), and CEB (UID/ BIO/04469/2013).info:eu-repo/semantics/publishedVersio