Color Filter Array Pattern Disturbance for Image Alteration Detection
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Solution Overview
Problem
Existing image processing techniques struggle to efficiently detect alterations in images, particularly internal copies, as they either disrupt the color filter array (CFA) pattern or require lengthy processing times when using feature amounts of partial regions.
Innovation Solution
An image processing apparatus and method that combines the detection of altered regions by extracting CFA patterns and feature amounts, using separate detectors for internal and external copies, and modifies the detection region based on these analyses to accurately identify alteration positions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If CFA pattern extraction is used to detect alteration, then external copy detection is improved, but internal copy detection capability deteriorates
Solution Approach 1:
The patent combines two different detection methods: CFA pattern extraction for detecting external copies and feature amount comparison for detecting internal copies. By merging these complementary approaches, the system achieves both high accuracy in external copy detection and capability in internal copy detection, resolving the contradiction between specialized performance and comprehensive coverage
2Adaptability or versatility
If feature amount comparison is used to detect alteration, then internal copy detection is improved, but processing time increases
Solution Approach 1:
The patent segments the detection process into two distinct pathways: one using CFA pattern extraction for external copy detection and another using feature amount comparison for internal copy detection. This segmentation allows each method to be applied selectively to appropriate cases, reducing overall processing time while maintaining comprehensive detection capability
3Adaptability or versatility
If both CFA pattern extraction and feature amount comparison are used, then detection comprehensiveness is improved, but processing complexity increases
Solution Approach 1:
The patent applies different detection methods to different local characteristics of image alterations: CFA pattern extraction is applied to regions suspected of external copying while feature amount comparison is applied to regions suspected of internal copying. This localized application of specialized methods reduces overall system complexity while maintaining comprehensive detection capability
Data Source
AI summary
A CFA pattern is extracted from captured image data for each first unit region. A first altered region is detected from disturbance of the periodicity of the CFA pattern, and the first altered region is an image region in which copying has been performed from image data different from the captured image data to the captured image data. The feature amount of the captured image data is extracted for each second unit region different in size from the first unit region. The feature amounts are compared for each second unit region to detect a second altered region, and the second altered region is an image region in which copying has been performed from the captured image data to the captured image data. Information concerning the first and second altered regions are output as alteration detection results in the captured image data.