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

Kirchner, Matthias

doi:10.1145/1411328.1411333

Cited by 206 publications

(131 citation statements)

References 15 publications

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“…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.…”

Section: Review Of the Major Resizing Detectorsmentioning

confidence: 99%

Detecting Trace of Seam Carving for Forensic Analysis

Ryu

Lee

2014

IEICE Trans. Inf. & Syst.

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SUMMARYSeam carving, which preserves semantically important image content during resizing process, has been actively researched in recent years. This paper proposes a novel forensic technique to detect the trace of seam carving. We exploit the energy bias and noise level of images under analysis to reliably unveil the evidence of seam carving. Furthermore, we design a detector investigating the relationship among neighboring pixels to estimate the inserted seams. Experimental results from a large set of test images indicates the superior performance of the proposed methods for both seam carving and seam insertion.

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Section: Review Of the Major Resizing Detectorsmentioning

confidence: 99%

Detecting Trace of Seam Carving for Forensic Analysis

Ryu

Lee

2014

IEICE Trans. Inf. & Syst.

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“…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.…”

Section: Tampering Detection Independent On the Type Of Forgerymentioning

confidence: 99%

Digital image forensics: a booklet for beginners

Redi

Taktak

Dugelay

2010

Multimed Tools Appl

307

115

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Digital visual media represent nowadays one of the principal means for communication. Lately, the reliability of digital visual information has been questioned, due to the ease in counterfeiting both its origin and content. Digital image forensics is a brand new research field which aims at validating the authenticity of images by recovering information about their history. Two main problems are addressed: the identification of the imaging device that captured the image, and the detection of traces of forgeries. Nowadays, thanks to the promising results attained by early studies and to the always growing number of applications, digital image forensics represents an appealing investigation domain for many researchers. This survey is designed for scholars and IT professionals approaching this field, reviewing existing tools and providing a view on the past, the present and the future of digital image forensics.

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“…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.…”

Section: Introductionmentioning

confidence: 99%

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

Thongkamwitoon

Muammar

Dragotti

2013

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

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This paper presents a novel theoretical framework for the reverse engineering of signal acquisition chains. We investigate how signals are transformed through a chain of signal acquisition and reconstruction stages. The signals, at different stages in the chain, are modelled using the theory of sampling signals with Finite Rate of Innovation (FRI). The model allows us to determine the chain structure and corresponding acquisition history from unknown query signals and to retrieve important parameters relating to the acquisition chain.

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

Cited by 206 publications

References 15 publications

Detecting Trace of Seam Carving for Forensic Analysis

Detecting Trace of Seam Carving for Forensic Analysis

Digital image forensics: a booklet for beginners

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

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