GAN-Powered Image Steganography: Combining NLP and Generative Adversarial Networks for Text and Voice Encryption

Security and privacy are essential in modern times particularly given the volume of private data that is shared between platforms. Conventional encryption methods frequently fall short in protecting many data kinds, including text, speech and graphics. By integrating Generative Adversarial Networks (GANs) and Natural Language Processing (NLP) a novel approach is put forth for creating an advanced image steganography system that integrates text and audio encryption. The project trains the GAN model using the ImageNet dataset which includes an extensive set of photos and labels. In the GAN architecture the autoencoder encodes images and the decoder reconstructs them. The pixel-wise error per pixel is 35.96 for S-error and 30.55 for C-error. By encoding hidden text into photographs this creative method makes image steganography more effective while masking the information from view. For text encryption news data is used to train a T5 model that is driven by NLP approaches. A user-friendly interface designed with streamlit is part of the solution which enables users to upload photos for encryption and enter text using speech recognition or typing.