Optimized detection and localization of copy-rotate-move forgeries using biogeography-based optimization algorithm.

Area of Science:

• Digital Image Processing
• Computer Vision
• Digital Forensics

Background:

• Image forgery is a growing concern due to accessible manipulation tools.
• Current methods struggle with complex forgeries like copy-rotate-move, lacking efficiency and robustness.
• Limitations exist in traditional feature extraction under severe transformations.

Purpose of the Study:

• To develop a novel, computationally efficient algorithm for detecting copy-rotate-move image forgeries.
• To enhance the robustness and accuracy of forgery detection against various transformations.
• To improve feature selection and matching for improved detection performance.

Main Methods:

• Integration of Radon Transform for feature extraction.
• Application of Biogeography-Based Optimization (BBO) for enhanced feature selection and matching.
• Rigorous evaluation on benchmark datasets.

Main Results:

• Superior performance in F1-score, recall, and accuracy compared to state-of-the-art methods.
• Demonstrated robustness against rotation and scale variations.
• Significant improvement in forgery localization with maintained computational efficiency.

Conclusions:

• The proposed Radon Transform and BBO integrated algorithm offers enhanced copy-rotate-move forgery detection.
• The method provides a computationally efficient and robust solution for digital forensics.
• This approach shows promise for real-world applications in combating image manipulation.