Abstract
This work proposes end-to-end trainable models of Generative Adversarial Networks (GAN) for hiding video data inside images. Hiding video inside images is a relatively new topic and has never been attempted earlier to our best knowledge. We propose two adversarial models that hide video data inside images: a base model with Recurrent Neural Networks and a novel model with 3D-spatiotemporal Convolutional Neural Networks. Both the models have two distinct networks: (1) An embedder to extract features from the time variate video data and inject them into the deep latent representations of the image. (2) An extractor that reverse-engineers the embedder function to extract the hidden data inside the encoded image. A multi-discriminator GAN framework with multi-objective training for multimedia hiding is one of the novel contributions of this work.
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Gandikota, R., Mishra, D., Brown, N.B. (2023). Hiding Video in Images: Harnessing Adversarial Learning on Deep 3D-Spatio-Temporal Convolutional Neural Networks. In: Gupta, D., Bhurchandi, K., Murala, S., Raman, B., Kumar, S. (eds) Computer Vision and Image Processing. CVIP 2022. Communications in Computer and Information Science, vol 1776. Springer, Cham. https://doi.org/10.1007/978-3-031-31407-0_5
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