Deep Learning-Based Steganalysis Against Spatial Domain Steganography

Against steganography to hide secret messages into an innocent-like cover, steganalysis was studied to detect the presence of hidden messages, and steganography flaws were determined by human intervention. In this paper, we present a steganalysis method using deep learning for spatial domain steganography which does not require human intervention. The deep learning-based steganalysis model is designed to have 1 high pass filter, 2 convolutional layers and 2 full connected layers. After being trained with cover images and LSB stego-images, unknown images are tested to determine if secret messages have been embedded. Experiments are performed using BOSS and SIPI database and the presented model shows 98% and 90% accuracy for LSB stego-images with the same key and different keys.