Identification Method for Digital Image Forgery and Filtering Region through Interpolation

Abstract

Because of the rapidly increasing use of digital composite images, recent studies have identified digital forgery and filtering regions. This research has shown that interpolation, which is used to edit digital images, is an effective way to analyze digital images for composite regions. Interpolation is widely used to adjust the size of the image of a composite target, making the composite image seem natural by rotating or deforming. As a result, many algorithms have been developed to identify composite regions by detecting a trace of interpolation. However, many limitations have been found in detection maps developed to identify composite regions. In this study, we analyze the pixel patterns of noninterpolation and interpolation regions. We propose a detection map algorithm to separate the two regions. To identify composite regions, we have developed an improved algorithm using minimum filer, Laplacian operation and maximum filters. Finally, filtering regions that used the interpolation operation are analyzed using the proposed algorithm.

FSM and RRU-net module for Image Splicing Forgery Detection

“…Most splicing forgery detection techniques are passive, meaning they don't rely on any kind of image prior knowledge [6]. Alahmadi et al [7] and Min and Dong [8] used DCT coefficients, minimum and maximum filter methods, and other techniques to extract characteristics from image blocks and detect splicing forgery. Numerous algorithms employ multiresolution techniques such as DWT [8].…”

“…Alahmadi et al [7] and Min and Dong [8] used DCT coefficients, minimum and maximum filter methods, and other techniques to extract characteristics from image blocks and detect splicing forgery. Numerous algorithms employ multiresolution techniques such as DWT [8]. Block matching is not, however, the sole technique used to identify splicing forgeries; SIFT characteristics are also utilized as a backup [9].…”

“…Jalab et al [14] obtained fractional entropy from DWT [15] coefficients, and SVM was used for classification. Min and Dong developed a novel tampering detection technique in [8] that relies on maximum and minimum filters. The minimum and maximum pixel differences between real and fake images are highlighted when a maximum filter and a minimum filter are combined.…”