Image Tampering Detection via Multi-Layer Feature Fusion
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Solution Overview
Problem
Existing forensic algorithms are ineffective in detecting deep learning-based image inpainting due to low detection accuracy and efficiency, as they fail to identify the tampered areas effectively.
Innovation Solution
A method and system that utilize a convolutional network architecture to analyze images by extracting residual data through filtering and obtaining multi-layer feature maps, which are then used to generate a tamper probability map, thereby determining the likelihood of image tampering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image forensics algorithms are used to detect deep learning-based image inpainting, then the detection process can be performed, but the detection accuracy is low and the algorithms are ineffective
Solution Approach 1:
The patent segments the image processing task by dividing the feature extraction into multiple layers (shallow features and deep features) and processing different regions independently. The convolutional network extracts features at multiple levels, with shallow layers capturing edge and texture information, and deep layers capturing semantic information. This multi-scale segmentation approach enables the detector to capture both fine-grained and coarse-grained tampering evidence, significantly improving detection accuracy for deep learning inpainting.
Solution Approach 2:
The patent transitions from traditional single-scale image patch analysis to multi-scale feature map analysis by introducing convolutional network layers. This adds a dimensional aspect of feature depth (multiple layers) to the traditional spatial analysis. The multi-layer feature maps provide hierarchical representation at different abstraction levels, enabling the detector to operate in an expanded feature space that captures both local and global tampering characteristics.
2Productivity
If single-size image patches and sliding window approaches are used, then the processing method is simple, but the detection efficiency is low
Solution Approach 1:
The patent implements dynamic feature extraction by using a convolutional network that adaptively learns feature representations at multiple scales rather than using fixed single-size patches. The network dynamically adjusts the receptive field and feature abstraction level for different regions, allowing efficient processing of varying tampering sizes and complexities. This dynamic approach replaces the static sliding window method with an adaptive feature extraction mechanism.
Solution Approach 2:
The patent replaces the mechanical sliding window approach with a neural network-based feature extraction system. Instead of manually sliding fixed-size windows across the image, the convolutional network automatically learns and extracts relevant features through trained filters and layers. This substitution of mechanical processing with intelligent learning-based processing significantly improves efficiency while capturing more discriminative tampering evidence.
3Measurement precision
If multi-layer feature maps are extracted and integrated, then the detection accuracy improves, but the computational complexity increases
Solution Approach 1:
The patent merges shallow and deep feature maps through concatenation and fusion operations in the convolutional network. By combining features from multiple layers, the network creates a comprehensive representation that leverages both local details (from shallow layers) and global context (from deep layers). This merging of multi-layer features enables accurate detection while the network's efficient architecture manages the computational load through shared weights and hierarchical processing.
Data Source
AI summary
The present application discloses a method, device, and system for testing an image to assess whether the image was subject to tampering. The method includes obtaining an image, extracting residual data of the image from the image, obtaining a feature map of at least two neural layers based at least in part on the residual data of the image, obtaining a tamper probability map of the image based at least in part on the feature map, and outputting the tamper probability map or providing an indication of whether the image was subject to tampering based at least in part on the tamper probability map.