PISAStego: Privacy-Preserving Instance-Specific Self-Supervised Autoencoder Steganography for Elevated Security in Industrial IoT Systems

The growing interconnectedness of industrial Internet of Things (IIoT) sensors and devices necessitates intelligent, robust, and lightweight security methods for transmitting private information. In order to overcome this difficulty in IIoT security, we propose PISAStego, a unique privacy-preserving instance-specific self-supervised convolutional autoencoder steganography. In our self-supervised PISAStego, a convolutional autoencoder architecture is used, where the encoder learns to distribute the secret information across visually less perceptible regions of the cover image. This concealment is processed by the reconstruction and smoothness constraints with the loss function in an instance-specific way, whereas the decoder effectively extracts the hidden data from the stego image with minimal loss. leaky rectified linear unit is incorporated to each convolutional layer in PISAStego to perform the nonlinearity to learn complex patterns, improve gradient flow, handle dead neurons, and preserve texture. Unlike conventional optimization-based or deep learning steganography models, the proposed model does not require any labeled data, large datasets, or longer training time. This makes the PISAStego a lightweight and faster algorithm. Our PISAStego algorithm has obtained an average 0.9959 normalized cross-correlation for hiding a $256 \times 256 \times 3$ secret image inside a $256 \times 256 \times 3$ cover image with 8 bits per pixel embedding capacity. Experimental results validate superior cost-effectiveness, lightweight with faster processing, and higher message hiding capacity compared to existing methods. Our model is lightweight, low in parameters, with higher embedding rate, which is ideal for providing robust authentication and faster covert communication to secure any IIoT system.