Profile resolution across multilayer networks through smartphone camera fingerprint

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

doi:10.1145/2790755.2790765

Cited by 14 publications

(10 citation statements)

References 23 publications

(29 reference statements)

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“…Several works proposed the use of PRNU to address a slightly different problem, i.e., to link social media profiles containing images and videos captured by the same sensor [11,12]. In particular, in [11] a hybrid approach investigates the possibility to identify the source of a digital video by exploiting a reference sensor pattern noise generated from still images taken by the same device.…”

Section: Device and Model Identification: Perfect Knowledge Methodsmentioning

confidence: 99%

“…The paper in [22] faces the problem of profile linking, also addressed by the perfect knowledge method in [12]; this time an unsupervised approach is used applying a kmedoids clustering. Differently from the other methods, that mainly use residual noise or PRNU to perform source identification, in [2,23,24] video file containers have been considered as hint for the identification and the classification of the device, the brand and the model of a device without a prior training phase.…”

Section: Device and Model Identification: Limited And Zero Knowledge Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Media forensics on social media platforms: a survey

Pasquini

Amerini

Boato

2021

EURASIP J. on Info. Security

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The dependability of visual information on the web and the authenticity of digital media appearing virally in social media platforms has been raising unprecedented concerns. As a result, in the last years the multimedia forensics research community pursued the ambition to scale the forensic analysis to real-world web-based open systems. This survey aims at describing the work done so far on the analysis of shared data, covering three main aspects: forensics techniques performing source identification and integrity verification on media uploaded on social networks, platform provenance analysis allowing to identify sharing platforms, and multimedia verification algorithms assessing the credibility of media objects in relation to its associated textual information. The achieved results are highlighted together with current open issues and research challenges to be addressed in order to advance the field in the next future.

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Section: Device and Model Identification: Perfect Knowledge Methodsmentioning

confidence: 99%

Section: Device and Model Identification: Limited And Zero Knowledge Methodsmentioning

confidence: 99%

Media forensics on social media platforms: a survey

Pasquini

Amerini

Boato

2021

EURASIP J. on Info. Security

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“…Through the paper, we call our dataset “Lab Dataset”, i.e.,

. Based on our previous work [ 40 ], the minimum number of images per samrtphone to get a reliable fingerprint is 50. Hence, for each smartphone, we collected 250 images.…”

Section: Methodsmentioning

confidence: 99%

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

2021

Self Cite

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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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“…A different method for PRNU fingerprint estimation [25] takes into account the effects of video compression on the PRNU noise selecting blocks of frames having at least one non-null discrete cosine transform (DCT) coefficient. Other works use PRNU to link social media profiles containing images and videos captured by the same sensor [9,26]. Similar approaches have been introduced for camera model identification [27,28].…”

Section: Forensic Analysismentioning

confidence: 99%

Identification of Social-Media Platform of Videos through the Use of Shared Features

Maiano

Amerini

Celsi³

et al. 2021

J. Imaging

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Videos have become a powerful tool for spreading illegal content such as military propaganda, revenge porn, or bullying through social networks. To counter these illegal activities, it has become essential to try new methods to verify the origin of videos from these platforms. However, collecting datasets large enough to train neural networks for this task has become difficult because of the privacy regulations that have been enacted in recent years. To mitigate this limitation, in this work we propose two different solutions based on transfer learning and multitask learning to determine whether a video has been uploaded from or downloaded to a specific social platform through the use of shared features with images trained on the same task. By transferring features from the shallowest to the deepest levels of the network from the image task to videos, we measure the amount of information shared between these two tasks. Then, we introduce a model based on multitask learning, which learns from both tasks simultaneously. The promising experimental results show, in particular, the effectiveness of the multitask approach. According to our knowledge, this is the first work that addresses the problem of social media platform identification of videos through the use of shared features.

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Profile resolution across multilayer networks through smartphone camera fingerprint

Cited by 14 publications

References 23 publications

Media forensics on social media platforms: a survey

Media forensics on social media platforms: a survey

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

Identification of Social-Media Platform of Videos through the Use of Shared Features

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