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Found 6 papers, 1 papers with code
Deep Learning-based Text-in-Image Watermarking
In this work, we introduce a novel deep learning-based approach to text-in-image watermarking, a method that embeds and extracts textual information within images to enhance data security and integrity.
Image-based Deep Learning for Smart Digital Twins: a Review
Smart Digital twins (SDTs) are being increasingly used to virtually replicate and predict the behaviors of complex physical systems through continual data assimilation enabling the optimization of the performance of these systems by controlling the actions of systems.
Padding Module: Learning the Padding in Deep Neural Networks
During the last decades, many studies have been dedicated to improving the performance of neural networks, for example, the network architectures, initialization, and activation.
FOD-A: A Dataset for Foreign Object Debris in Airports
This paper presents the creation methodology, discusses the publicly available dataset extension process, and demonstrates the practicality of FOD-A with widely used machine learning models for object detection.
DLWIoT: Deep Learning-based Watermarking for Authorized IoT Onboarding
DLWIoT embeds user credentials into carrier images (e. g., QR codes printed on IoT devices), thus enables IoT onboarding only by authorized users.
An Automated and Robust Image Watermarking Scheme Based on Deep Neural Networks
Digital image watermarking is the process of embedding and extracting a watermark covertly on a cover-image.
