Image Steganography: Digital Information Embedding Using Singular Value Decomposition and Simulation Software - Suha Aritonang; Uvi Desi Fatmawati (Prodi Teknik Elektro, Fakultas Teknik Unika Atma Jaya Jakarta, 2025)
Singular Value Decomposition (SVD) is a matrix decomposition technique that is widely used in digital signal and image processing because of its ability to represent important information efficiently. This study aims to explore the use of the SVD method in the steganography and watermarking process of digital images as part of efforts to improve the security of multimedia information. The approach used involves inserting hidden data in the form of images, sentences, and paragraphs into the host image by modifying the singular value elements of the image matrix decomposition results. Various scenarios are simulated, including inserting RGB format watermarks into grayscale images and vice versa, by testing variations in the insertion parameter (α). Evaluation is carried out on the visual quality of the resulting image (imperceptibility), as well as the success rate of information extraction. The experimental results show that this method can insert information without causing significant visual distortion and still maintain high message extraction accuracy. This study confirms the effectiveness and flexibility of the SVD technique as an information insertion method that can be applied in digital copyright protection systems and visual data security. This method also has the potential to be integrated with other techniques in more complex and robust watermarking systems.