A Fast Keypoint Based Hybrid Method for Copy Move Forgery Detection
Sunil Kumar, J. V. Desai, Shaktidev Mukherjee

TL;DR
This paper introduces a hybrid keypoint-based method for copy-move forgery detection that improves speed and robustness against transformations like rotation, scaling, noise, and compression, outperforming existing techniques.
Contribution
A novel hybrid approach combining SURF and BRISK features enhances detection speed and invariance to common post-processing operations in copy-move forgery detection.
Findings
Significantly faster detection compared to existing SURF-based methods.
Robustness against rotation, scaling, noise, and JPEG compression.
Effective in large images with post-processing manipulations.
Abstract
Copy move forgery detection in digital images has become a very popular research topic in the area of image forensics. Due to the availability of sophisticated image editing tools and ever increasing hardware capabilities, it has become an easy task to manipulate the digital images. Passive forgery detection techniques are more relevant as they can be applied without the prior information about the image in question. Block based techniques are used to detect copy move forgery, but have limitations of large time complexity and sensitivity against affine operations like rotation and scaling. Keypoint based approaches are used to detect forgery in large images where the possibility of significant post processing operations like rotation and scaling is more. A hybrid approach is proposed using different methods for keypoint detection and description. Speeded Up Robust Features (SURF) are…
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Taxonomy
MethodsSPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings
