Detection of Steganographic Threats Targeting Digital Images in Heterogeneous Ecosystems Through Machine Learning

Steganography is increasingly exploited by malware to avoid detection and to implement different advanced offensive schemes. An attack paradigm expected to become widely used in the near future concerns cloaking data in innocent-looking pictures, which are normally used by several devices and applications, for instance to enhance the user experience. Therefore, with the increasing popularity of application stores, availability of cross-platform services, and the adoption of various devices for entertainment and business duties, the chances for hiding payloads in digital pictures multiply in an almost unbounded manner. To face such a new challenge, this paper presents an ecosystem exploiting a classifier based on Deep Neural Networks to reveal the presence of images embedding malicious assets. Collected results indicated the effectiveness of the approach to detect malicious contents, even in the presence of an attacker trying to elude our framework via basic obfuscation techniques (i.e., zip compression) or the use of alternative encoding schemes (i.e., Base64). Specifically, the achieved accuracy is always ~100% with minor decays in terms of precision and recall caused by the presence of additional information caused by compression.