PRNU-Based Forgery Localization in a Blind Scenario

Cozzolino, Davide; Marra, Francesco; Poggi, Gianfranco; Sansone, Carlo; Verdoliva, Luisa

doi:10.1007/978-3-319-68548-9_52

Cited by 9 publications

(3 citation statements)

References 18 publications

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“…It is worth noting that this approach can be also extended to blind scenarios, where no prior information about the camera is known provided a suitable clustering procedure identifies the images which share the same PRNU [96], [106].…”

Section: B One-class Sensor-based and Model-based Methodsmentioning

confidence: 99%

Media Forensics and DeepFakes: an overview

Verdoliva¹

2020

Preprint

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With the rapid progress of recent years, techniques that generate and manipulate multimedia content can now guarantee a very advanced level of realism. The boundary between real and synthetic media has become very thin. On the one hand, this opens the door to a series of exciting applications in different fields such as creative arts, advertising, film production, video games. On the other hand, it poses enormous security threats. Software packages freely available on the web allow any individual, without special skills, to create very realistic fake images and videos. So-called deepfakes can be used to manipulate public opinion during elections, commit fraud, discredit or blackmail people. Potential abuses are limited only by human imagination. Therefore, there is an urgent need for automated tools capable of detecting false multimedia content and avoiding the spread of dangerous false information. This review paper aims to present an analysis of the methods for visual media integrity verification, that is, the detection of manipulated images and videos. Special emphasis will be placed on the emerging phenomenon of deepfakes and, from the point of view of the forensic analyst, on modern data-driven forensic methods. The analysis will help to highlight the limits of current forensic tools, the most relevant issues, the upcoming challenges, and suggest future directions for research.

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Section: B One-class Sensor-based and Model-based Methodsmentioning

confidence: 99%

Media Forensics and DeepFakes: an overview

Verdoliva¹

2020

Preprint

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“…It is possible to model the strong spatial dependencies present in an image through a Markov Random Field so as to make joint rather than isolated decisions [16], or to rely on discriminative random fields [12] and multi-scale analysis [43]. It is worth noting that the PRNU-based approach can be also extended to blind scenarios, where no prior information about the camera is known provided a suitable clustering procedure identifies the images which share the same PRNU [20,21]. It is even possible to recover some information about PRNU by estimating it from a single image or a group of frames in a video [51,53,60].…”

Section: Prnu-based Approachmentioning

confidence: 99%

Multimedia Forensics Before the Deep Learning Era

Cozzolino

Verdoliva

2022

Advances in Computer Vision and Pattern Recognition

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Image manipulation is as old as photography itself, and powerful media editing tools have been around for a long time. Using such conventional signal processing methods, it is possible to modify images and videos obtaining very realistic results. This chapter is devoted to describe the most effective strategies to detect the widespread manipulations that rely on traditional approaches and do not require a deep learning strategy. In particular, we will focus on manipulations like adding, replicating, or removing objects and present the major lines of research in multimedia forensics before the deep learning era and the rise of deepfakes. The most popular approaches look for artifacts related to the in-camera processing chain (camera-based clues) or the out-camera processing history (editing-based clues). We will focus on methods that rely on the extraction of a camera fingerprint and need some prior information on pristine data, for example, through a collection of images taken from the camera of interest. Then we will shift to blind methods that do not require any prior knowledge and reveal inconsistencies with respect to some well-defined hypotheses. We will also briefly review the most interesting features of machine learning- based methods and finally present the major challenges in this area.

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“…Korus et al [29] proposed a PRNU-based tampering localization method using a multi-scale fusion approach. In turn, Cozzolino et al [30] proposed a blind localization method; however, it still needs an image dataset in advance for the clustering module. As an alternative, deep learningbased methods have commanded much attention in recent years.…”

Section: Introductionmentioning

confidence: 99%

Statistical Analysis of Signal-Dependent Noise: Application in Blind Localization of Image Splicing Forgery

Zou¹,

Yao

Qin

et al. 2020

Preprint

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Visual noise is often regarded as a disturbance in image quality, whereas it can also provide a crucial clue for imagebased forensic tasks. Conventionally, noise is assumed to comprise an additive Gaussian model to be estimated and then used to reveal anomalies. However, for real sensor noise, it should be modeled as signal-dependent noise (SDN). In this work, we apply SDN to splicing forgery localization tasks. Through statistical analysis of the SDN model, we assume that noise can be modeled as a Gaussian approximation for a certain brightness and propose a likelihood model for a noise level function. By building a maximum a posterior Markov random field (MAP-MRF) framework, we exploit the likelihood of noise to reveal the alien region of spliced objects, with a probability combination refinement strategy. To ensure a completely blind detection, an iterative alternating method is adopted to estimate the MRF parameters. Experimental results demonstrate that our method is effective and provides a comparative localization performance.

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PRNU-Based Forgery Localization in a Blind Scenario

Cited by 9 publications

References 18 publications

Media Forensics and DeepFakes: an overview

Media Forensics and DeepFakes: an overview

Multimedia Forensics Before the Deep Learning Era

Statistical Analysis of Signal-Dependent Noise: Application in Blind Localization of Image Splicing Forgery

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