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Advancing Copy-Move Manipulation Detection in Complex Image Scenarios Through Multiscale Detector
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DOI：10.1109/ACCESS.2024.3397466.png)
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Abstract
En 中文
This research presents a new approach for identifying instances of copy-move forgeries in digital images by utilizing the Multiscale Detector a Neural Network-based method, which serves as an image key-point detector and descriptor. The act of copy-move manipulation involves the replication and subsequent insertion of a specific segment of an image, intending to modify the overall content of the image. The approach we utilize leverages the sophisticated functionalities of Multiscale Detector, a framework that combines key-point detection with descriptor extraction, to accurately detect and localize instances of copy-move manipulation. The effectiveness of our approach is assessed on a range of copy-move forgeries, encompassing instances that have undergone post-processing and geometric transformations. The experimental findings illustrate the resilience of our approach in identifying instances of manipulations over a diverse range of textured images and various alteration approaches. Furthermore, our approach demonstrates strong performance even when subjected to supplementary processing procedures such as brightness modification, color reduction, contrast adjustment, and blurring. Our suggested method has greater performance when compared to the existing manipulation detection approach, as demonstrated through a comparative analysis. In addition, the algorithm we have developed has high computing efficiency, allowing for real-time detection of forgeries. The methodology employed in this study, which builds upon the Multiscale Detector framework, offers a highly effective approach to the detection of copy-move manipulations in digital images.
Keywords:
Forgery
Feature extraction
Digital images
Image coding
Image quality
Image color analysis
Neural networks
Computer vision
Detection algorithms
Copy-move forgery
multiscale detector
neural network
computer vision
image forensics
forgery detection