Stegan detection method for latent images for intellectual property protection systems

Abstract

Objective. Improving the quality of detection and stego-detection of latent images embedded in the protected object of intellectual property by various methods. Method. The method for stego-detection of latent images based on deep learning is proposed. The method is based on the use of the VGG16 convolutional neural network model, in which the architecture and training parameters are optimized. Result. Increasing the accuracy of detecting stegocontainer images by 3.8%, as well as the possibility of using the algorithm of the developed method for images with a higher resolution than the dimension of the input of an artificial neural network. Conclusion. The developed method is intended for stegan detection in two cases: to detect the fact of illegal use of intellectual property objects; for use in computer forensics when identifying images containing hidden and prohibited information.