Image Authenticity Detection Using Fixed Pattern Noise
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Solution Overview
Problem
Conventional image authenticity detection methods are prone to inaccuracies due to the influence of image background changes, illumination variations, occlusions, and facial expressions, making it difficult to ensure robustness and accuracy in detecting image authenticity.
Innovation Solution
An image authenticity detection method that involves removing low-frequency information, denoising the image, and analyzing the distribution of fixed pattern noise inherent to camera sensors, which is not affected by image content, to determine the authenticity of the image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image content features are used for authenticity detection, then the detection method can identify image characteristics, but the detection accuracy deteriorates due to interference from background changes, illumination variations, occlusions, and facial expressions
Solution Approach 1:
The patent extracts and isolates the fixed pattern noise component from the image by removing low-frequency information and applying denoising operations. This separates the sensor-specific noise pattern (which contains authenticity information) from the image content and environmental variations, allowing detection based solely on the extracted noise features that are independent of background, illumination, and other interfering factors
Solution Approach 2:
The patent transforms the image from the spatial domain to the frequency domain by removing low-frequency information, thereby changing the representation parameters. This transformation allows the detection system to focus on high-frequency noise components that contain sensor-specific patterns while filtering out low-frequency content that carries background and illumination information, thus improving detection accuracy by changing the operational parameter domain
2Reliability
If conventional image content features are analyzed, then the detection process can proceed with available image data, but the robustness deteriorates due to sensitivity to environmental and content variations
Solution Approach 1:
The patent converts the previously harmful fixed pattern noise (which was considered interference) into a beneficial feature for detection. By recognizing that this noise is sensor-specific and invariant to environmental changes, the patent transforms it into a reliable biomarker for authenticity detection, turning what was once a source of degradation into the key indicator for identifying genuine versus forged images
Solution Approach 2:
The patent extracts the fixed pattern noise component from the image by removing low-frequency information and applying denoising operations. This separation isolates the sensor-specific noise pattern from image content and environmental variations, creating a robust feature set that maintains consistent characteristics across different lighting conditions, backgrounds, and image transformations, thereby improving detection reliability
Data Source
AI summary
This disclosure is directed to an image authenticity detection method and apparatus. The method includes: obtaining an image; removing low-frequency information from the image to obtain first image information of the image; denoising the first image information to obtain second image information; determining, based on a difference between the first image information and the second image information, a fixed pattern noise feature map corresponding to the image; analyzing distribution of fixed pattern noise in the fixed pattern noise feature map, the fixed pattern noise being inherent noise from a camera sensor and not interfered by image content; and detecting, based on the distribution, authenticity of the image to obtain an authenticity detection result of the image.