VisionStego: An AI-Guided Dual-Layer Cryptographic Steganography Framework for Secure Cloud Data Transmission

Abstract

Modern cloud platforms now carry a substantial share of the world's sensitive digital traffic, and defending that traffic calls for more than hiding its meaning - an intercepted ciphertext is still visibly a target worth attacking. Encryption alone protects content; it does nothing to disguise the fact that a secret is being sent at all. This paper proposes VisionStego, a duallayer security architecture that pairs symmetric-key encryption with an artificial-intelligence-guided steganographic embedding stage, so that cloud-hosted data is protected in both substance and appearance. The secret payload is first reduced in size through wavelet-based compression, then encrypted with a shared symmetric key, and finally concealed inside a cover image at positions chosen by a trained Convolutional Neural Network (CNN) rather than by a fixed or pseudo-random rule. The network scores each candidate pixel for embedding suitability using edge strength, local variance, and texture complexity, concentrating modification in regions where it is least visible to the eye and least anomalous to statistical steganalysis. Tested on the Lena, Baboon, and Peppers benchmark images, the resulting stego images reach a Peak Signal-to-Noise Ratio (PSNR) of up to 46.2 dB and a Structural Similarity Index (SSIM) of 0.985, exceeding conventional LSB, adaptive LSB, and wavelet-based baselines on every image and every metric tested. These findings indicate that VisionStego offers a practical route to the combined concealment and confidentiality that neither cryptography nor steganography can deliver in isolation.