Gradient-Guided Adaptive Perceptual Steganography using Deep Learning

Lightweight CNNs and masking based on gradient are applied here in this paper's enhanced picture steganography method for added robustness and imperceptibility. The current method adjusts the embedding strength adaptively using the analysis of image gradients in each local region with minimal perceptual distortion and big payload size. Experimental assessment reflects that the suggested method outperforms classical frequency-domain and space-domain Steganographic methods when it comes to peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM). Due to its flexibility and computational efficiency, this approach is appropriate for real-world secure communication applications.