The method overcomes physical signs of pixel modifications while achieving a high data payload. This technique enables data to be hidden in a cover image, while the image recognition artificial neural network checks the presence of any visible alterations on the stego-image.
Introduction based on its features. Image classification came into existence for by training the computer with the data. The image classification is based on the content of the vision. Motivation by [1], in this paper,
This paper proposed and implemented an image steganography technique (proposed DCT LSB-2) that does not interfere with existing image recognition neural network systems. This technique enables data to be hidden in a cover image, while the image recognition ANN checks the presence of any visible alterations on the stego-image.