A Multimodal Approach to Digital Security: Combining Steganography, Watermarking, and Image Enhancement

This study addresses the limitations of traditional Least ‎Significant Bit (LSB) techniques in digital forensics by ‎proposing an intelligent watermarking framework enhanced ‎with Contrast-Limited Adaptive Histogram Equalization ‎‎(CLAHE) and Spread Spectrum Watermarking. We propose a ‎robust hybrid approach combining Discrete Cosine Transform ‎‎(DCT)-based frequency domain watermarking with spread ‎spectrum techniques to improve imperceptibility, security, and ‎resilience against unauthorized tampering. The proposed ‎model embeds robust watermarks with minimal impact on ‎perceptual image quality, validated through quantitative ‎metrics such as Mean Squared Error (MSE) and perceptual ‎analysis. Comparative experiments demonstrate superior ‎performance over conventional LSB methods, particularly in ‎resisting compression and noise-based attacks. Additionally, ‎we integrate cryptographic hashing (SHA-256) for ‎authentication, ensuring tamper-proof verification. The results ‎highlight the framework’s efficacy in digital forensics, ‎copyright protection, and secure multimedia communication. ‎Future work explores adaptive watermarking with machine ‎learning and blockchain integration for enhanced traceability‎.