Image tampering localization via estimating the non-aligned double JPEG compression

TLDR: An efficient method to locate the forged parts in a tampered JPEG image by estimating the shift of NA-DJPEG compression and it doesn't need an image dataset to train a machine learning based classifier or to get a proper threshold.

Abstract: In this paper, we present an efficient method to locate the forged parts in a tampered JPEG image The forged region usually undergoes a different JPEG compression with the background region in JPEG image forgeries When a JPEG image is cropped to another host JPEG image and resaved in JPEG format, the JPEG block grid of the tampered region often mismatches the JPEG block grid of the host image with a certain shift This phenomenon is called non-aligned double JPEG compression (NA-DJPEG) In this paper, we identify different JPEG compression forms by estimating the shift of NA-DJPEG compression Our shift estimating approach is based on the percentage of non zeros of JPEG coefficients in different situations Compared to previous work, our tampering location method (i) performances better when dealing with small image size, (ii) is robust to common tampering processing such as resizing, rotating, blurring and so on, (iii) doesn't need an image dataset to train a machine learning based classifier or to get a proper threshold