![]() Here c and s are the cover and secret images respectively, and β is how to weigh their reconstruction errors We use a weighted L2 loss function along with Adam optimizer for training the model.The model is trained for 100 epochs suing a batch size of 8. ![]() We then hide this transformed image inside the input cover image using the hide block, to generate the container image.Finally, in the reveal block we decode the container image to produce the secret output.Therefore, the training graph has two inputs and two outputs. Prepare block, Hide block, Reveal block.In prepare block, we transform the color-based pixels to more useful features for succinctly encoding the images. Since we are using a autoencoder based architecture, the labels are same as their corresponding inputs. ![]() We train the hiding and reveal networks simultaneously in the form of an autoencoder, using keras.The model has two inputs corresponding to a pair of secret and cover image and two outputs corresponding to their inputs. Must learn how to extract and reconstruct the same information from the encoded message, with Information from the secret image into the least noticeable portions of the cover image and then, it The technique used is imageĬompression through auto-encoding networks.The trained system must learn to compress the Revealing processes and are designed to specifically work as a pair. Deep neural networks are simultaneously trained to create the hiding and Our main goal is to hide a full size (N*N RGB) color image within another image ![]() The implementation will be done using keras, with tensorflow backend.Also, we will be using random images from imagenetdataset for training the model.We will use 50000 images (RGB-224x224) for training and 7498 images for validation. Here we plan to extend the basic implementation from the paper 'Hiding images in plain sight: Deep steganography' to videos, i.e we will train a model for hiding videos within other videos using convolutional neural networks.Also, we will incorporate additional block-shuffling as an encryption method for added security and other image enhancement techniques for improving the output quality. In frequency domain, we change some mid-frequency components in the frequency domain.These heuristics are effective in the domains for which they are designed, but they are fundamentally static and therefore easily detected.We can evaluate a steganographic technique or algorithm by using performance and qualtiy metrics like capacity, secrecy, robustness, imperceptibility, speed, applicabilty etc. Steganography on images can be broadly classified as spatial domain steganography and frequency domain steganography.In spatial domain, algorithms directly manipulate the values ( least significant bits) of some selected pixels. Steganalysis is the study of detecting messages hidden using steganography (breaking) this is analogous to cryptanalysis applied to cryptography.Steganography is used in applications like confidential communication, secret data storing, digital watermarking etc. It is often combined with cryptography to improve the security of the hidden message. The main aim of steganogrpahy is to prevent the detection of a hidden message. In modern steganography, the goal is to covertly communicate a digital message. Steganography is the practice of concealing a secret message within another, ordinary, message.The messages can be images, text, video, audio etc. Deep Video Steganography: Hiding Videos in Plain SightĪ convolutional neural network for hiding videos inside other videos.It is implemented in keras/tensorflow using the concepts of deep learning, steganography and encryption.
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