Digital Image Encryption Using Logistic Map

This study focuses on the application of the Logistic Map algorithm in the Python programming language for digital image encryption and decryption. It investigates the impact of image type, image size, and Logistic Map parameter values on computational speed, memory usage, encryption, and decryption results. Three image sizes (300px x 300px, 500px x 500px, and 1024px x 1024px) in TIFF, JPG, and PNG formats are considered. The Digital Image Encryption and Decryption process utilizes the Logistic Map algorithm implemented in Python. Various parameter values are tested for each image type and size to analyze the encryption and decryption outcomes. The findings indicate that image type does not affect memory usage, which remains consistent regardless of image type. However, image type significantly influences Decryption results and computation time. Notably, the TIFF image type exhibits the fastest computation time, with durations of 0.17188 seconds, 0.28125 seconds, and 1.10938 seconds for 300px x 300px, 500px x 500px, and 1024px x 1024px images, respectively. Additionally, the encryption results vary depending on the image type. The Logistic Map algorithm is unable to restore encryption results accurately for JPG images. Furthermore, the research highlights that higher values of x, Mu, and Chaos lead to narrower histogram values, resulting in improved encryption outcomes. This study contributes to the field by exploring the application of the Logistic Map algorithm in Python and analyzing the effects of image type, image size, and Logistic Map parameter values on computation time, memory usage, and Digital Image Encryption and Decryption results. Prior research has not extensively addressed these aspects in relation to the Logistic Map algorithm in Python