Deep Learning Based Image Steganalysis

Various cryptographic and steganographic techniques are used to hide digital information during its processing, storage, and transmission. While cryptography hides the information content of digital data (by converting them into a meaningless set of noise-like sequences), steganography hides the very existence of information messages. In other words, steganographic techniques hide digital messages by embedding them in so-called. containers. Containers are other digital data or physical objects. To do this, containers (covers, media) must be highly redundant data. Revealing the fact of steganographic hiding and detecting an embedded message is usually extremely difficult. In fact, hidden messages are some noise added to the container, and we must, based on the study of this noise, decide on the presence or absence of an embedded message. In this article, we consider deep learning methods for steganoanalysis of digital cover images. We have considered several deep learning models and conduct numerous tests on various datasets. Our experiments show that deep learning does indeed make it possible to design effective stego-detectors, but this requires fine-tuning of model hyperparameters and optimization of the neural network architecture.