Steganography detection and payload classification using multitask and transfer learning

Abstract

Steganography, or the concealment of information in digital media, is a credible cyber threat as it allows concealed communication, data exfiltration, and cyber spying. As compared to cryptographic methods for concealing information via encryption, steganographic methods conceal information inside such a format that the digital carriers are viewed visually unscathed and hence difficult to find and available for forensic examination. This paper solves two fundamental issues of steganalysis: image quality assessment (IQA) and payload classification, which are essential in enhancing cyber defense and forensic analysis of the digital world. The Author introduces a mechanism that integrates transfer learning and multitask learning approaches to solve these issues. Dependence on the representational ability of pre-trained convolutional neural networks (CNNs), our method trains these models on multitasks of payload classification and IQA. The model can find various steganographic payloads, i.e., JMiPOD, and UERD, and evaluate the degree of distortions introduced due to steganographic embedding. Multitask learning architecture enables the model to achieve generalization of learned features across tasks but with reduced computational complexity. Verification experiments confirm the efficiency of our solution. The payload classifier module obtained an accuracy of 90%, strongly showing excellent discrimination between various steganographic techniques.