With growing storage and the diffusion of multimedia data across digital networks, protecting visual content is a subject of interest in research, especially via the means of image content obscuration methods. Although several of these techniques have been explored to provide basic to advanced protection against re-identification by humans or automated recognition systems, few achieve full key-based reversibility while introducing minimal distortion to the resulting image. In this paper, we propose a novel image content obscuration method that leverages variational autoencoders. Our approach reduces the dimensionality of images into latent vectors of a source class, and then applies three distinct transformations to the latent representation to match a target class. These transformations are designed to be both visually imperceptible and reversible using a secret key, enabling the original content to be accurately reconstructed. We evaluate our method through qualitative and quantitative experiments regarding the obscuration and defense against re-identification, and compare to previous image obscuration methods.