Fals-Ism: A Graph Isomorphism Framework for Multi-Level Detection of Falsified PDF Documents

Abstract

Fake Portable Document Format (PDF) documents are disseminated in an incredible rhythm across social media. Negative incidences are obvious but effective solutions identifying falsified items in the PDF are still in need. Unlike determining malicious scripts inserted into the file, this research aims at identifying falsified objects from different layers of the document. Specifically, we introduce Fals-Ism, a novel approach to detect falsified PDF documents based on graph isomorphism. Each document is transformed and characterized by metadata, structure, and content required to build the corresponding graph such that any alteration is reflected on the complete graph. The graph is input to the isomorphism search algorithm namely; VF2 to verify if there is a similarity-based isomorphism. Experiments are conducted on (36) PDF documents considering metadata, structure, and content modifications. The results show that Fals-Ism (i) Is efficient to detect forgery at metadata level, structure, and content; (ii) Is robust and resistant to forgery attacks such as insertion, deletion, and modification of information; (iii) Does not require certain information about the PDF documents beforehand to perform the detection. Fals-Ism can detect different types of falsifications in PDF (version 1.7 or higher) with an accuracy of 90%. A comparison with similar work confirms that Fals-Ism could be a complementary tool for fake news detection.