An intelligent security framework integrating fuzzy logic–driven image enhancement with CNN for robust steganography

This study presents an intelligent security framework that integrates fuzzy logic-driven image enhancement with Convolutional Neural Networks (CNNs) for robust steganography and image analysis. The proposed framework focuses on adaptive image enhancement, intelligent image evaluation, and secure data embedding in perceptually less sensitive edge regions to improve imperceptibility and embedding robustness. It integrates fuzzy logic and CNN-based analysis to perform adaptive image enhancement, intelligent image classification, and edge-aware steganographic embedding within a unified security architecture. Experimental evaluation is performed using images from the SecObjects and SecStanfordDogs datasets. The effectiveness of the proposed framework is quantitatively assessed using Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) metrics. Comparative analysis demonstrates that the proposed approach provides an efficient and reliable framework for intelligent image security and robust steganographic applications.