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Digital Image Forgery Detection Based on GLCM and HOG Features

Liya Baby, Ann Jose

Millions of digital documents are produced day by day. With the availability of powerful computers and advanced photo editing softwares, the manipulation of digital images become very easy. For decades photographs are used as evidence in courts, but the increased use of computer graphics and image processing techniques undermines the trust in photographs. The driving forces of forgery detection are the need of authenticity and to maintain the integrity of the image. In this paper, the most common photographic manipulation technique known as image composition or splicing is considered. Forgery detection is machine learning based and minimal user interaction is required. Both texture and edge features are extracted from the illuminant map of an image and the result is obtained by performing classification. Here kNN classifier is used to classify whether the image is original or forgered.