Hybrid Transformer-Based Steganography Using Frequency Embedding and Adversarial Robustness

Abstract

Steganography is the practice of hiding secret information within digital media such as images, audio, or video. It ensures confidential communication by concealing the existence of data itself, unlike encryption. Modern research focuses on improving invisibility, security, and resistance to detection using AI and deep learning. This project aims to design a secure and intelligent image steganography system using a hybrid Transformer model. It focuses on increasing data hiding capacity while maintaining image quality and reducing detectability. The system will be capable of resisting steganalysis attacks, compression, and noise distortions in real-world usage. Recent studies show that deep-learning steganography models still suffer from low robustness and poor scalability. Most approaches fail under compression or noise, and their hidden data can be detected by advanced AI models. High computational cost, limited payload capacity, and dataset dependency further affect their reliability. This project introduces a hybrid Transformer integrated with Discrete Cosine Transform (DCT) for frequency embedding. By combining spatial and frequency domains, it ensures better concealment and robustness. Adversarial training with a steganalysis discriminator will enhance security against modern detection models. The system will achieve higher PSNR and SSIM scores, proving superior imperceptibility and accuracy. Implementation will use Python, PyTorch, and OpenCV for model training and image processing. Datasets like COCO, BOSSBase, and ImageNet will be used for evaluation. Performance metrics such as PSNR, SSIM, MSE, and BER will measure quality and accuracy. Development and testing will be carried out in Jupyter Notebook or Google Colab environments.