Using Artificial Neural Network to Test Image Covert Communication Effect

Abstract

—Hacking social or personal information is rising, and data security is given serious attention in any organization. There are several data security strategies depending on what areas it is applied to, for instance, voice, image, or video. Image is the main focus of this paper; hence, this paper proposed and implemented an image steganography (covert communication) technique that does not break existing image recognition neural network systems. This technique enables data to be hidden in a cover image while the image recognition Artificial Neural Network (ANN) checks the presence of any visible alterations on the stego-image. Two different image steganography methods were tested: Least Significant Bit (LSB) and proposed Discrete Cosine Transform (DCT) LSB-2. The resulting stego-images were analyzed using a neural network implemented in the Keras TensorFlow soft tool. The results showed that the proposed DCT LSB-2 encoding method allows a high data payload and minimizes visible alterations, keeping the neural network’s efficiency at a maximum. An optimum ratio for encoding data in an image was determined to maintain the high robustness of the steganography system. This proposed method has shown improved stego-system performance compared to the previous techniques.