{'fraction_non_alphanumeric': 0.01645021645021645, 'fraction_numerical': 0.0017316017316017316, 'mean_word_length': 5.316939890710382, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Forgery Detection Based on Intrinsic Document Contents

Nowadays, Document forgery detection is becoming increasingly important as forgery techniques are becoming available even to untrained users. Hence, documents that do not contain any extrinsic security features (e.g. invoices) have become easier to forge. We previously presented a method to detect manipulated documents based on distortions introduced during the forgery creation process. In this paper, several approaches are explored to improve accuracy and time taken to detect forgeries based on document distortions. The main idea behind the presented approaches is to automatically identify which parts of a document belong to the template (and hence would remain static across different documents originating from the same source) and then detect distortions in those parts only. An improvement up to 29% in accuracy of forgery detection is observed compared to our previous work. Furthermore, we also present an approximation of the original method that results in a reduction in run time of the method by several orders of magnitude, while having only a marginal reduction in its accuracy.