Camera model identification based on forensic traces extracted from homogeneous patches

Bennabhaktula, Guru Swaroop; Alegre, Enrique; Karastoyanova, Dimka; Azzopardi, George

doi:10.1016/j.eswa.2022.117769

Cited by 11 publications

(3 citation statements)

References 25 publications

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“…This method aids in tracing images back to their precise device of origin, supporting investigations, authentication, and ensuring the integrity of digital evidence in various contexts. 4…”

Section: Image Source Identificationmentioning

confidence: 99%

“…By leveraging advanced techniques like machine learning and statistical modeling, these minute discrepancies are isolated and associated with a specific individual device. This method aids in tracing images back to their precise device of origin, supporting investigations, authentication, and ensuring the integrity of digital evidence in various contexts 4 …”

Section: Image Source Identificationmentioning

confidence: 99%

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A hybrid data fusion approach with twin CNN architecture for enhancing image source identification in IoT environment

Singh,

Sehgal

2024

Computational Intelligence

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With the proliferation of digital devices in internet of things (IoT) environment featuring advanced visual capabilities, the task of Image Source Identification (ISI) has become increasingly vital for legal purposes, ensuring the verification of image authenticity and integrity, as well as identifying the device responsible for capturing the original scene. Over the past few decades, researchers have employed both traditional and machine‐learning methods to classify image sources. In the current landscape, data‐driven approaches leveraging deep learning models have emerged as powerful tools for achieving higher accuracy and precision in source prediction. The primary focus of this research is to address the complexities arising from diverse image sources and variable quality in IoT‐generated multimedia data. To achieve this, a Hybrid Data Fusion Approach is introduced, leveraging multiple sources of information to bolster the accuracy and robustness of ISI. This fusion methodology integrates diverse data streams from IoT devices, including metadata, sensor information, and contextual data, amalgamating them into a comprehensive data set for analysis. This study introduces an innovative approach to ISI through the implementation of a Twin Convolutional Neural Network Architecture (TCA) aimed at enhancing the efficacy of source classification. In TCA, the first CNN architecture, referred to as DnCNN, is employed to eliminate noise from the original data set, generating 256 × 256 patches for both training and testing. Subsequently, the second CNN architecture is employed to classify images based on features extracted from various convolutional layers using a 3 × 3 filter, thereby enhancing prediction efficiency. The proposed model demonstrates exceptional accuracy in effectively classifying image sources, showcasing its potential as a robust solution in the realm of ISI.

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“…This method aids in tracing images back to their precise device of origin, supporting investigations, authentication, and ensuring the integrity of digital evidence in various contexts. 4…”

Section: Image Source Identificationmentioning

confidence: 99%

Section: Image Source Identificationmentioning

confidence: 99%

A hybrid data fusion approach with twin CNN architecture for enhancing image source identification in IoT environment

Singh,

Sehgal

2024

Computational Intelligence

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“…On the contrary, in 2020, Taspinar et al investigated about source camera identification considering that digital images may have been captured using different resolutions and aspect ratios [18]. In 2021, Bennabhaktula et al proposed a CNN-based method for capture device identification, which extracts from natural images homogeneous regions with very little scene information [19]. Most recently, in 2022, Xiao et al reported a PRNU extraction method that, based on a densely connected hierarchical denoising network (DHDN), allows to identify the source capture devices (digital cameras or smartphone) for natural digital images, which were obtained from the Dresden and Daxing databases [20].…”

Section: Introductionmentioning

confidence: 99%

Identifying the Digital Camera from Natural Images Using Residual Noise and the Jensen–Shannon Divergence

Rodríguez-Santos

Reséndiz

Delgado-Gutiérrez

et al. 2022

Journal of Electrical and Computer Engineering

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Regarding the problem of digital camera identification, many methods have been proposed, and for several of them, their effectiveness has been verified on the basis of disputed flat images. However, in real cases the disputed images are natural images, rather than flat images. In that case, several of the already proposed methods are not effective. Hence, in this paper, a method is proposed for the digital camera identification from natural images based on the statistical comparison between the residual noise in the natural disputed images and the fingerprint defined for the eligible digital cameras. In the reported case studies, the HDR database provided by the Communications and Signal Processing Laboratory of University of Florence is used to select a set of eligible digital cameras, and from this image database, for each digital camera, a set of disputed flat images, a set of disputed natural images, and a set of flat reference images were selected. Thus, the fingerprint of each digital camera was calculated from the probability density function (PDF) of the photo-response nonuniformity (PRNU) extracted from its reference images. Therefore, in order to identify the source digital camera of a natural disputed image, the Jensen–Shannon divergence (JSD) was implemented to statistically compare the PRNU-based fingerprint of each eligible source camera against the noise residual of that disputed image. The proposed method has a similar effectiveness to methods based on the peak-to-correlation energy or the Kullback–Leibler divergence when the disputed images are flat images and the PRNU is considered, but it is significantly more effective than those methods when the disputed images are natural images.

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SCCRNet: a framework for source camera identification on digital images

Sychandran,

Shreelekshmi

2023

Neural Comput & Applic

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Camera model identification based on forensic traces extracted from homogeneous patches

Cited by 11 publications

References 25 publications

A hybrid data fusion approach with twin CNN architecture for enhancing image source identification in IoT environment

A hybrid data fusion approach with twin CNN architecture for enhancing image source identification in IoT environment

Identifying the Digital Camera from Natural Images Using Residual Noise and the Jensen–Shannon Divergence

SCCRNet: a framework for source camera identification on digital images

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