Image Authenticity Detection Based on the Local Features and Similarity Features
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Keywords:
Image Forgery, Local Features, Similarity Features, Geometric Transformation, Scale Invariant Feature TransformAbstract
Picture is better than tons of words but in this era of modern technology, Forgery has become
very popular. Due to the use of digital editing tools it becomes very easy to manipulate someone’s personal
information. Mostly forgery has been done so accurately that it becomes tough to identify the parts where
forgery has attacked. Copy Move forgery detection is used to identify the local variants in image where
forgery has been attacked. The existing methods are used to check the authenticity of the images has
performed results on limited data sets or sets of images. We purpose a novel forensics technique that is used
to extract features from small regions or parts of images. Scale Invariant Feature Transform (SIFT) is a
feature detector and descriptor that is used to extract local features in the images. After detect the blobs or
corner point in the image, the next step is to extract different features using SIFT. These feature vectors of
the images are supposed to very different and invariant to rotation, blurring, noisy, and different geometric
transformations. The proposed method gives a good result at MICC_F220, MICC-F2000, and MIC
F8MULTI. Our algorithm is good at detecting very small forged parts in the foreground and background
of the image. Moreover, the proposed method improves the accuracy of the algorithm and gives good
satisfactory performance.
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