High-performance image forgery detection via adaptive SIFT feature extraction for low-contrast or small or smooth copy–move region images

Sujin, J. S.; Sophia, S.

doi:10.1007/s00500-023-08209-6

Cited by 8 publications

(1 citation statement)

References 33 publications

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“…Sheng Zhang and Wei Zhu [9] proposed a SIFT matching algorithm combined with RANSAC; this method uses the SIFT algorithm to extract and match the feature points of an image sequence, and then uses the RANSAC algorithm to eliminate the mismatched points, which improves the matching correctness rate to a certain extent, but there is also the problem of high resource occupancy in the process of descriptor generation. J. S. Sujin et al improved image quality by adjusting the contrast thresholding and scaling the image, which they achieved by adjusting the size and contrast thresholding factors to produce a sufficient number of keypoints and enhance the correctness of the feature point matching, even in smooth, low-contrast, or small regions [10].…”

Section: Introductionmentioning

confidence: 99%

Research on a Fast Image-Matching Algorithm Based on Nonlinear Filtering

Yin,

Zhang,

Hao

et al. 2024

Algorithms

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Computer vision technology is being applied at an unprecedented speed in various fields such as 3D scene reconstruction, object detection and recognition, video content tracking, pose estimation, and motion estimation. To address the issues of low accuracy and high time complexity in traditional image feature point matching, a fast image-matching algorithm based on nonlinear filtering is proposed. By applying nonlinear diffusion filtering to scene images, details and edge information can be effectively extracted. The feature descriptors of the feature points are transformed into binary form, occupying less storage space and thus reducing matching time. The adaptive RANSAC algorithm is utilized to eliminate mismatched feature points, thereby improving matching accuracy. Our experimental results on the Mikolajcyzk image dataset comparing the SIFT algorithm with SURF-, BRISK-, and ORB-improved algorithms based on the SIFT algorithm conclude that the fast image-matching algorithm based on nonlinear filtering reduces matching time by three-quarters, with an overall average accuracy of over 7% higher than other algorithms. These experiments demonstrate that the fast image-matching algorithm based on nonlinear filtering has better robustness and real-time performance.

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Section: Introductionmentioning

confidence: 99%