Seamless Copy–Move Replication in Digital Images

Abstract

The importance and relevance of digital-image forensics has attracted researchers to establish different techniques for creating and detecting forgeries. The core category in passive image forgery is copy–move image forgery that affects the originality of image by applying a different transformation. In this paper, a frequency-domain image-manipulation method is presented. The method exploits the localized nature of discrete wavelet transform (DWT) to attain the region of the host image to be manipulated. Both patch and host image are subjected to DWT at the same level l to obtain 3l+1 sub-bands, and each sub-band of the patch is pasted to the identified region in the corresponding sub-band of the host image. Resulting manipulated host sub-bands are then subjected to inverse DWT to obtain the final manipulated host image. The proposed method shows good resistance against detection by two frequency-domain forgery detection methods from the literature. The purpose of this research work is to create a forgery and highlight the need to produce forgery detection methods that are robust against malicious copy–move forgery.

Tampering Detection and Segmentation Model for Multimedia Forensic

“…The resampling attack generally occasionally allows correlation among pixels because of interpolation. The CNN-based [16], [17], image forgery identification model learns resampling features [18] very well using spatial maps produced through translation invariance of various regions of images [19], [20]. Thus, this research work aims to build an efficient resampling feature detection through CNN to detect hybrid attacks and achieve better-tampered region segmentation outcomes [21], [22].…”