Towards the Detectability of Image Steganography Using NNPRTOOL (Neural Network for Pattern Recognition)

Towards the Detectability of Image Steganography Using NNPRTOOL (Neural Network for Pattern Recognition)

연구 분야: Strategies

학회: International Conference on Intelligent Systems and Pattern Recognition

The term steganography represent the science of hiding secret information in a carrier medium like Image, Audio, Video, etc. This field has been participated in multidisciplinary applications and different sectors. For instance, in Healthcare, E-commerce, Access control, and Databases. According to literature, some of those applications, and their analytical, classification data and results are studied. This include current challenges of detectability which can be done with or without using a machine learning classifier. In this research, an additional experiment of Neural Network Pattern Recognition (NNPRTOOL) is applied to classify original and stego images belong to two main classes and directories. This experiment contributes in the robustness of steganography in general. Moreover, it highlights the evolution of image steganalysis research using a machine learning classifier. However, the potential of such tools like NNPRTOOL, Support Vector Machine (SVM), and ENSEMBLE Classifier have shown significant results in image classifications.