Fast and reliable resampling detection by spectral analysis of fixed linear predictor residue

Abstract

This paper revisits the state-of-the-art resampling detector, which is based on periodic artifacts in the residue of a local linear predictor. Inspired by recent findings from the literature, we take a closer look at the complex detection procedure and model the detected artifacts in the spatial and frequency domain by means of the variance of the prediction residue. We give an exact formulation on how transformation parameters influence the appearance of periodic artifacts and analytically derive the expected position of characteristic resampling peaks. We present an equivalent accelerated and simplified detector, which is orders of magnitudes faster than the conventional scheme and experimentally shown to be comparably reliable.

Detecting Trace of Seam Carving for Forensic Analysis

“…Aside from analyzing the n-th order derivative of the interpolated image, Popescu and Farid identified the correlation among neighboring pixels by a local linear predictor that adopts the expectation/maximization (EM) algorithm [6]. In [7], Kirchner analytically derived the relation between the derivative-based detector and Popescu's detector. From the analysis, he proposed an improved resampling detector.…”

Digital image forensics: a booklet for beginners

“…The detection of such patterns is eventually considered as an evidence of forgery. Detector [80] is further improved by Kirchner in [53] from the computational point of view. Although its effectiveness is proven on regular images, it is observed in [80] that the method is weaker when dealing with heavily compressed images, where further periodicity is caused by quantization effects.…”

Reverse engineering of signal acquisition chains using the theory of sampling signals with finite rate of innovation

“…The paper develops a resampling detection algorithm based on the correlation of periodic artefacts from resampling with those of neighboring pixels. It was further studied in the spectral domain using artifacts from the residue of a local linear predictor [3]. In [4], the roles of prefiltering and cyclostationarity of signals are studied in order to improve the performance of the resampling detection algorithm.…”