An enhance embedding method using edge and textures detection for image steganography

Embedding a secret image in steganography without causing distortion or detection is challenging due to image size constraints, which require compression. This compression can lead to content loss and makes the hidden data vulnerable to steganalysis. Current methods often struggle to adequately balance between embedding capacity, content integrity, and detection robustness, highlighting the need for improved steganography techniques. Therefore, this study propose a method that can balance concealment, image size, compression, robustness, and security in order to handle these challenges. The study aims to enhance image steganography techniques by using reversible color transformations and optimal area selection within image mosaics to resist statistical attacks. This method seeks to ensure secure and effective data concealment by identifying and utilizing noisy zones in images, making it more difficult for steganalysis tools to detect the embedded information. The study utilized differential operators and filters to detect edges and textures in images, where data embedding is less perceptible. The least significant bit (LSB) matching method was applied to embed secret information. The effectiveness of this approach was measured through Peak Signal-to-Noise Ratio, Root Mean Square Error, and Structural Similarity Index Measure, with histogram analysis used to evaluate embedding capacity and method effectiveness. The findings reveal that the proposed method significantly enhances the robustness and security of image steganography, achieving an average Peak Signal-to-Noise Ratio of 18.839 dB and a Structural Similarity Index of 0.647. By embedding in noisy zones using edge and texture detection which complicates feature extraction, making hidden information more secure and statistically less detectable than basic LSB matching techniques. The study contributes both theoretically and practically to the field of steganography by developing an innovative algorithm that enhances the security and robustness of image hidden data. It has practical applications in various fields such as intellectual property protection, secure communication, and cybersecurity. Future research could focus on integrating diverse steganographic methods to create even more robust solutions