Smartphone Verification and User Profiles Linking Across Social Networks by Camera Fingerprinting

Bertini, Flavio; Sharma, Rajesh; Iannì, Andrea; Montesi, Danilo

doi:10.1007/978-3-319-25512-5_12

Cited by 6 publications

(4 citation statements)

References 15 publications

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“…Hence, in this paper, we use a different approach based on supervised and unsupervised classification techniques, extending our previous works [ 37 , 38 , 39 ]. What makes our work innovative is using images from one SN to identify smartphones applied on another SN, which provides user profile linking across different SN platforms.…”

Section: Related Workmentioning

confidence: 99%

No Matter What Images You Share, You Can Probably Be Fingerprinted Anyway

2021

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The popularity of social networks (SNs), amplified by the ever-increasing use of smartphones, has intensified online cybercrimes. This trend has accelerated digital forensics through SNs. One of the areas that has received lots of attention is camera fingerprinting, through which each smartphone is uniquely characterized. Hence, in this paper, we compare classification-based methods to achieve smartphone identification (SI) and user profile linking (UPL) within the same or across different SNs, which can provide investigators with significant clues. We validate the proposed methods by two datasets, our dataset and the VISION dataset, both including original and shared images on the SN platforms such as Google Currents, Facebook, WhatsApp, and Telegram. The obtained results show that k-medoids achieves the best results compared with k-means, hierarchical approaches, and different models of convolutional neural network (CNN) in the classification of the images. The results show that k-medoids provides the values of F1-measure up to 0.91% for SI and UPL tasks. Moreover, the results prove the effectiveness of the methods which tackle the loss of image details through the compression process on the SNs, even for the images from the same model of smartphones. An important outcome of our work is presenting the inter-layer UPL task, which is more desirable in digital investigations as it can link user profiles on different SNs.

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Section: Related Workmentioning

confidence: 99%

No Matter What Images You Share, You Can Probably Be Fingerprinted Anyway

2021

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“…Ensuring the homogeneity of the embedding region R, the embedding strength JNDL-based is obtained with respect to the optimal mean value µ R , as shown in Equation (12).…”

Section: Inputmentioning

confidence: 99%

“…Since the beginning of the digital era and the rise of social media, many digital images and videos have been manipulated and shared without restrictions, control, and protection, creating copyright protection and intellectual propriety problems [1][2][3][4]. As a solution, several visible and invisible digital watermarking algorithms have been proposed mainly for images [5][6][7], video, audio [8,9], and other related fields [10][11][12]. However, in realworld applications such as banknotes, official documents, etc., the watermarks are used to deliver information about its authenticity.…”

Section: Introductionmentioning

confidence: 99%

“…An embedding strength that allows embedding imperceptibly a visible watermark pattern is required; in this way, the JNDL function [29] is considered to estimate the embedding strength and ensure the watermark imperceptibility of the new improved IVW algorithm. Thus, if the appropriate color channel to embed the watermark pattern depends on ensuring that the largest homogeneous region R has a variance value nearest to 0, the JNDL-based embedding strength can be obtained with respect to the corresponding mean value µ R , as shown in Equation (12).…”

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Imperceptible–Visible Watermarking to Information Security Tasks in Color Imaging

et al. 2021

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Digital image watermarking algorithms have been designed for intellectual property, copyright protection, medical data management, and other related fields; furthermore, in real-world applications such as official documents, banknotes, etc., they are used to deliver additional information about the documents’ authenticity. In this context, the imperceptible–visible watermarking (IVW) algorithm has been designed as a digital reproduction of the real-world watermarks. This paper presents a new improved IVW algorithm for copyright protection that can deliver additional information to the image content. The proposed algorithm is divided into two stages: in the embedding stage, a human visual system-based strategy is used to embed an owner logotype or a 2D quick response (QR) code as a watermark into a color image, maintaining a high watermark imperceptibility and low image-quality degradation. In the exhibition, a new histogram binarization function approach is introduced to exhibit any watermark with enough quality to be recognized or decoded by any application, which is focused on reading QR codes. The experimental results show that the proposed algorithm can embed one or more watermark patterns, maintaining the high imperceptibility and visual quality of the embedded and the exhibited watermark. The performance evaluation shows that the method overcomes several drawbacks reported in previous algorithms, including geometric and image processing attacks such as JPEG and JPEG2000.

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Shared Images and Camera Fingerprinting May Lead to Privacy Issues

Rouhi

Bertini

Montesi

2020

Advanced Sciences and Technologies for Security Applications

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the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Smartphone Verification and User Profiles Linking Across Social Networks by Camera Fingerprinting

Cited by 6 publications

References 15 publications

No Matter What Images You Share, You Can Probably Be Fingerprinted Anyway

No Matter What Images You Share, You Can Probably Be Fingerprinted Anyway

Imperceptible–Visible Watermarking to Information Security Tasks in Color Imaging

Shared Images and Camera Fingerprinting May Lead to Privacy Issues

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