Metadata-Based Audio and Image Tampering Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for detecting alterations in audio and image data are ineffective when data is converted from digital to analog, as digital signatures are lost, and can be manually modified to appear unchanged, compromising security and accuracy in critical applications.
Innovation Solution
The use of metadata characteristics, including semantic descriptions, to detect alterations by comparing original and test data sets, identifying changes and locations of alterations through processing and analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If digital signatures are used to detect data alteration, then detection accuracy is improved, but the method becomes ineffective when data is converted to analog form
Solution Approach 1:
The patent introduces metadata as an intermediary layer between the digital data and the detection system. Metadata characteristics serve as a mediator that can be extracted from both digital and analog data, enabling the detection system to operate across different data formats without requiring direct manipulation of the original data signatures.
Solution Approach 2:
The patent creates a copy of the data's identifying characteristics through metadata extraction. Instead of relying on the original digital signature which is lost in analog conversion, the system extracts and stores metadata characteristics that replicate the essential identifying features, allowing for later comparison and detection of alterations.
2Object-generated harmful factors
If digital signatures are manually modified, then data tampering is achieved, but the detection system can no longer distinguish between original and altered data
Solution Approach 1:
The patent segments the data into multiple independent components: the actual data content and the metadata characteristics. By separating these components, the system can detect alterations in the metadata without being affected by manual modifications to the data itself, maintaining detection reliability even when digital signatures are compromised.
Solution Approach 2:
The patent performs preliminary extraction and storage of metadata characteristics before any potential tampering occurs. This preliminary action establishes a baseline of authentic characteristics that can be compared against future versions, enabling the system to detect even subtle alterations before they become problematic.
3Reliability
If metadata characteristics are extracted and compared, then detection of alterations is improved, but processing complexity increases
Solution Approach 1:
The patent extracts only the essential metadata characteristics from the data, filtering out unnecessary information. By taking out only the critical identifying features and storing them separately, the system reduces processing complexity during comparison operations while maintaining high detection reliability.
Data Source
AI summary
Using metadata to detect alteration of data. A first set of metadata characteristics including at least one respective semantic description are recorded for a first set of data representing original data. A second set of metadata characteristics including at least one corresponding semantic description are recorded for a second set of data representing data under test. The first and second sets of metadata characteristics are compared. If the first and second sets of metadata characteristics are not identical, these sets are processed to identify locations in the first set of data that have been altered. Using the at least one semantic description for the first set of data and the at least one corresponding semantic description for the second set of data, one or more metadata characteristics that have changed from the first set of data to the second set of data are identified.