Web25 de nov. de 2024 · Recently data hiding using deep neural networks were introduced [10, 11, 13]. Zhu et al. has introduced the adversarial component in data hiding using an encoder-decoder model to embed and extract respectively. Most of the works [10,11,12,13] deal with hiding images which are stationary signals and 3 dimensional. Web21 de nov. de 2024 · Data hiding is the procedure of encoding desired information into a certain types of cover media (e.g. images) to resist potential noises for data recovery, while ensuring the embedded image has few perceptual perturbations. Recently, with the tremendous successes gained by deep neural networks in various fields, the research …
High-Capacity Reversible Data Hiding using Deep Learning
Web22 de dez. de 2024 · Hiding data has always been of significant importance to digital forensics. Previously, steganography has been combined with cryptography and neural … Web14 de jul. de 2024 · As a large number of images are transmitted through social networks every moment, terrorists may hide data into images to convey secret data. Various … greencastle card show
ECCV 2024 Open Access Repository
WebSteganography is an art and science of invisible communication, achieved by hiding secret information in a cover object (image, audio, video, text). According to the communication cover/carrier forms, steganography can … Web3 de jan. de 2024 · Zhu et al. proposed another GAN-based model of Hiding Data With Deep Networks (HiDDeN), whose overall network structure is similar to the Hayes model, but added with different noise layers. This model not only generates adaptive steganographic images by the network, but also resists multiple attacks, and thus can … Web10 de jan. de 2024 · Data hiding with deep neural networks (DNNs) has experienced impressive successes in recent years. A prevailing scheme is to train an autoencoder, consisting of an encoding network to embed ... flowing hair silver dollar value