Understanding benford's law and its vulnerability in image forensics

Wang, Jingwei; Cha, Byung-Ho; Cho, Seong Ho; Kuo, C.-C. Jay

doi:10.1109/icme.2009.5202811

Cited by 9 publications

(5 citation statements)

References 8 publications

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“…A similar assumption is adopted by many approaches aiming at detecting double JPEG compression. More precisely, some of the proposed works rely on detecting the violation of the so-called Benford's law (also known as FD law or significant digit law) [20]. Many fraud detection algorithms departs from the assumption that the analyzed data are well modeled by pre-defined stochastic models that present peculiar characteristics.…”

Section: B) the Fd Lawmentioning

confidence: 99%

Discriminating multiple JPEG compressions using first digit features

Milani

Tagliasacchi

Tubaro

2014

SIP

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I . I N T R O D U C T I O NThe recent development of multimedia devices and editing tools, together with the proliferation of communication infrastructures and content sharing applications, has made the acquisition, the editing, and the diffusion of images and videos relatively-easy tasks. A single multimedia content downloaded from the internet can be the outcome of a long chain of processing steps. This fact introduces several concerns about the origin, the authenticity, and the trustability of images and videos downloaded from the network [1,2]. Moreover, identifying the origin and the authenticity (i.e., the absence of alterations after acquisition) of images proves to be a crucial element in court cases for the validation of evidences [3]. Moreover, detecting alterations permits inferring an objective quality evaluation of the analyzed multimedia content.From these premises, multimedia forensic analysts have been recently focusing on detecting alterations on images since they can be easily acquired and modified even by a non-expert user. In order to fulfill this task, many of the proposed works aim at identifying images, which have been compressed more than once estimating the coding parameters that characterize the coding stages that precede the last one [4]. This fact is justified by the observation that most Dipartimento di Elettronica, Informazione, e Bioingegneria (DEIB), Politecnico di Milano, P.za Leonardo da Vinci 32, 20133 Milano, ItalyCorresponding author: S. Milani Email: simone.milani@polimi.it of the digital multimedia contents are available in compressed format. Indeed, most of the images distributed over the internet are coded according to the JPEG standard [5].All the solutions proposed in the literature aim at detecting double compression on images assuming that some alterations can be performed between the first and the second compression stages. However, we believe that this assumption does not hold in many practical scenarios since analyzing the feasible processing chains for a given downloaded image it is deducible that more than two compression stages may have been applied. As an illustrative example, let us consider an image, which is originally compressed by the acquisition device (i.e., a video or photo camera) to be stored in the onboard memory. A second compression is performed by the owner, after editing the image to enhance the perceptual quality and adjust the format (e.g., brightness/contrast adjustment, rescaling, cropping, color correction, etc.). A third compression is performed whenever the content is uploaded to a blog or to an on-line photo album. As a matter of fact, it is reasonable to assume that a large number of digital images available online have gone through more than two compression stages performed by its owner, and could be further compressed by other users. In these cases, a method that identifies the number of compression stages proves to be extremely important in reconstructing the processing history of a content [6].This paper aims at identifying traces of...

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Section: B) the Fd Lawmentioning

confidence: 99%

Discriminating multiple JPEG compressions using first digit features

Milani

Tagliasacchi

Tubaro

2014

SIP

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“…It is observed that many empirical data in the real world follow Benford's law such as stock market indices, tax returns and Fibonacci numbers (Wang, Cha, Cho, & JayKuo, 2009). Moreover, digital analysis (also first-digit law or Benford's Law) was discussed in the professional guidance on fraud detection in general (Debreceny & Gray, 2010).…”

Section: Formulas For Expected Digital Frequenciesmentioning

confidence: 99%

Benford’s Law and an Analysis in Istanbul Stock Exchange (BIST)

Karavardar¹

2014

IJBM

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Benford's Law, which has a logaritmic base, is a simple but an effective analytical examination tool for researchers. It makes researchers determine abnormalities in numerical data clusters. Benford's Law as a numerical analytic test is a mathematical comparison which proves unnatural deviations in data analysis. For that reason, it has various application areas such as auditing and finance. Investors may use Benford's Law for finding out financial frauds and abuses. This paper aims to test stock market indexes and stock values in Istanbul Stock Exchange (BIST) whether they fellow Benford's Law of Anomalous Numbers or not. All indexes' closing values and stocks' closing values, stocks' monthly gains and losses were also examined. In this study, it was found that series of monthly gains and losses on the twenty-eight indexes reasonably agree with Benford's Law.

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“…The morphing detector adopted in [5] is based on the hypothesis that unlike naturally generated data, manipulated data do not obey the Benford's law. Although high accuracy in detection of morphed random face images obtained using Benford's law, however, they note that using legitimate image processing techniques, one can create face images with similar Benford feature distribution [5,6].…”

Section: Introductionmentioning

confidence: 99%

Topological Data Analysis for Image Tampering Detection

Asaad

Jassim

2017

Digital Forensics and Watermarking

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This paper introduces a topological approach to detection of image tampering for forensics purposes. This is based on the emerging Topological Data Analysis (TDA) concept of persistent homological invariants associated with certain image features. Image features of interest are pixels that have a uniform Local Binary pattern (LBP) code representing texture feature descriptors. We construct the sequence of simplicial complexes for increasing sequence of distance thresholds whose vertices are the selected set of pixels, and calculate the corresponding non-increasing sequence of homology invariants (number of connected components). The persistent homology of this construction describes the speed with which the sequence terminates, and our tamper detection scheme exploit its sensitivity to image tampering/degradation. We test the performance of this approach on a sufficiently large image dataset from a benchmark dataset of passport photos, and show that the persistent homology sequence defines a discriminating criterion for the morphing attacks (i.e. distinguishing morphed images from genuine ones).

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Understanding benford's law and its vulnerability in image forensics

Cited by 9 publications

References 8 publications

Discriminating multiple JPEG compressions using first digit features

Discriminating multiple JPEG compressions using first digit features

Benford’s Law and an Analysis in Istanbul Stock Exchange (BIST)

Topological Data Analysis for Image Tampering Detection

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