PDF Forgery Detection via Incremental Update Comparison
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Solution Overview
Problem
Existing methods fail to effectively detect digital forgeries in PDF documents without requiring additional information, despite the increasing use of PDF format for fraudulent purposes.
Innovation Solution
A computer-implemented method and system that utilizes incremental updates within PDF documents to iteratively remove updates, compare visual differences, and detect regions of modification by analyzing the PDF document itself, employing image processing and optical character recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If additional information (such as digital watermarking or barcodes) is embedded in PDF documents for forgery detection, then the reliability of forgery detection is improved, but the device complexity and ease of operation deteriorate
Solution Approach 1:
The patent applies the self-service principle by enabling PDF documents to detect their own forgeries using intrinsic incremental update information already present in the document structure. The system analyzes the document's own version history and modification records without requiring external watermarking or additional embedded data, thus maintaining reliability while reducing complexity
Solution Approach 2:
The patent extracts and utilizes the incremental update information that is already embedded within the PDF document structure. By taking out and analyzing this existing version control data, the system achieves forgery detection without adding external watermarking layers or barcodes, thereby avoiding the complexity associated with those approaches
2Reliability
If additional information (such as digital watermarking or barcodes) is embedded in PDF documents for forgery detection, then the reliability of forgery detection is improved, but the ease of operation deteriorates
Solution Approach 1:
The system enables automatic self-detection of forgeries by analyzing the document's own incremental update structure. Users simply need to submit the PDF file without any manual preparation or embedding of detection markers, making the process as easy as traditional PDF viewing while achieving reliable forgery detection
Solution Approach 2:
The system extracts detection information directly from the document's existing incremental update metadata. This approach eliminates the need for users to manually add or manage watermarking layers or barcodes, maintaining operational simplicity comparable to standard PDF handling while enabling reliable automated forgery detection
3Productivity
If incremental updates are used for their original purpose of performance optimization, then the productivity of PDF editing is improved, but the ability to detect forgeries deteriorates
Solution Approach 1:
The patent converts the previously unused incremental update information, which was originally created solely for performance optimization, into a valuable resource for forgery detection. By analyzing the version history and modification records embedded in these updates, the system transforms a performance feature into a security feature, enabling reliable forgery detection while maintaining the original productivity benefits
Solution Approach 2:
The patent makes the incremental update mechanism serve multiple functions: it continues to provide performance benefits for efficient PDF editing and modification, while simultaneously enabling forgery detection through analysis of the version control information. This multi-functionality resolves the contradiction by making the same structural feature beneficial for both productivity and security
Data Source
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AI summary
System and computer-implemented method for detecting digital forgeries in PDF documents, the method comprising: receiving (104) an input document d0 (102) in PDF format; analyzing (106) the input document d0 (102) to detect incremental updates (108); iteratively selecting (110) a number i of incremental updates last included in the input document d0 (102), and for each iteration: obtaining (112) a previous version di (114) of the input document d0 (102) by removing the selected number i of last incremental updates (108) from the input document d0 (102); detecting (116) visual differences between the input document d0 (102) and the previous version di (114); and detecting (128) regions ri (120) in the documents (d0,di) containing the visual differences; and providing (124) the regions ri (120) detected in at least one iteration as an output (212).