SE-Attn StegaVAE: a lightweight dual branch based variational auto-encoder with multi-objective loss for image steganography

Information security is crucial with the increasing data and internet usage. In data communication, Imaging data is one of the most frequently used data types for communication, especially in intelligence operations and in law enforcement use cases. Steganography is a method for hiding secrets in cover images, followed by complex image encryption and decryption methods. Previously, many Deep Learning (DL) methods have been proposed on steganography and achieved competitive results. However, there is still a need for lightweight, computationally less expensive DL models. Therefore, a computationally inexpensive, lightweight dual-branch based Variational Auto Encoder (VAE) model is proposed, namely SE-Attn StegaVAE. In this model, the Squeeze and Excitation (SE) block, Attention, and skipping connections are effectively used, whereas a sequentially optimal way is adapted to hide the secret in attention-oriented cover samples. Furthermore, a multi-objective loss function is proposed to penalize the model to hide secrets effectively and reconstruct them without any loss of information. In this study, three experiments have been performed with three different bit embeddings (2, 4, and 8) on the DIV2K dataset utilizing the SE-Attn StegaVAE model. Two-fold validation-based testing results outperformed as compared to State-of-the-Art (SOTA) methods and proven to be more error-free, computationally less expensive, with competitive similarity scores.