Social Network Identification Through Image Classification With CNN

Amerini, Irene; Li, Chang-Tsun; Caldelli, Roberto

doi:10.1109/access.2019.2903876

Cited by 47 publications

(32 citation statements)

References 25 publications

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“…Others papers dealing with the distinction among different kind of devices such as scanner, digital camera, computer generated content are proposed in [8]- [10]. A new trend in recent years for the device identification is related to investigate about the social networks provenance of digital images [11], [12].…”

Section: Related Workmentioning

confidence: 99%

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Smartphones Identification Through the Built-In Microphones With Convolutional Neural Network

Baldini

Amerini

2019

IEEE Access

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The use of mobile phones or smartphones has become so widespread that most people rely on them for many services and applications like sending e-mails, checking the bank account, accessing cloud platforms, health monitoring, buying on-line and many other applications where sharing sensitive data is required. As a consequence, security functions are important in the use of smartphones, especially because most of the applications require the identification and authentication of the device in mobility. This is usually achieved through cryptographic systems but recent research studies have also investigated alternative or complementary authentication mechanisms which can be used to strengthen cryptographic methods with multi-factor authentication. In this paper, we investigate the identification and the authentication of smartphones using the intrinsic physical properties of the mobile phones built-in microphones. The possibility to identify a microphone on the basis of features extracted from audio recordings is well known in literature but it is mostly used in forensics studies and usually relies on human voice recordings. On the contrary this paper proposes a smartphone identification and authentication approach by stimulating the built-in microphone with non-voice sounds at different frequencies. An extensive data set of 32 phones was used to evaluate experimentally the proposed approach. On the basis of the proven performance of deep learning techniques, a new Convolutional Neural Network architecture is proposed both for the identification and the authentication purposes. Its performance, in comparison to other machine learning algorithms, is demonstrated in presence of different types of noises (e.g., Gaussian White noise, Babble noise and Street noise). Satisfactory results have been obtained showing that the exploitation of a fingerprint from the microphone sensor is a good choice to assess smartphone distinctiveness. INDEX TERMS Smartphone identification, authentication, microphone, machine learning, deep learning. IRENE AMERINI (M'17) received the Laurea degree in computer engineering and the Ph.D. degree in computer engineering, multimedia, and telecommunication from the University of Florence, Italy, in 2006 and 2010, respectively.

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Section: Related Workmentioning

confidence: 99%

“…For the baseline algorithm we fixed the following optimal values: the SVM grid search optimization provided values of C = 2 12 and γ = 2 6 while the optimal value of K for the KNN was set to K = 1.…”

Section: A Optimizationmentioning

confidence: 99%

Smartphones Identification Through the Built-In Microphones With Convolutional Neural Network

Baldini

Amerini

2019

IEEE Access

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“…Taking this into account, the post’s image and hashtags were used to classify emotions of the SNS user [ 30 ]. Another study analyzed images posted on SNS and classified the SNS platforms to which the images were posted, as the impact on cybersecurity is significant according to SNS platforms [ 31 ].…”

Section: Related Workmentioning

confidence: 99%

Enhancing Personalized Ads Using Interest Category Classification of SNS Users Based on Deep Neural Networks

Hong

Choi

Lim

et al. 2020

Sensors

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The classification and recommendation system for identifying social networking site (SNS) users’ interests plays a critical role in various industries, particularly advertising. Personalized advertisements help brands stand out from the clutter of online advertisements while enhancing relevance to consumers to generate favorable responses. Although most user interest classification studies have focused on textual data, the combined analysis of images and texts on user-generated posts can more precisely predict a consumer’s interests. Therefore, this research classifies SNS users’ interests by utilizing both texts and images. Consumers’ interests were defined using the Curlie directory, and various convolutional neural network (CNN)-based models and recurrent neural network (RNN)-based models were tested for our user interest classification system. In our hybrid neural network (NN) model, CNN-based classification models were used to classify images from users’ SNS postings while RNN-based classification models were used to classify textual data. The results of our extensive experiments show that the classification of users’ interests performed best when using texts and images together, at 96.55%, versus texts only, 41.38%, or images only, 93.1%. Our proposed system provides insights into personalized SNS advertising research and informs marketers on making (1) interest-based recommendations, (2) ranked-order recommendations, and (3) real-time recommendations.

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“…Recently, Quan et al [9] showed that by using convolutional methods it is possible to recognize Instagram filters and attenuate the sensor pattern noise signal in images. Amerini et al [10] introduced a CNN for learning distinctive features among social networks from the histogram of the discrete cosine transform (DCT) coefficients and the noise residual of the images. Phan et al [11] proposed a method to track multiple image sharing on social networks by using a CNN architecture able to learn a combination of DCT and metadata features.…”

Section: Introductionmentioning

confidence: 99%

Learning Double-Compression Video Fingerprints Left From Social-Media Platforms

Amerini

Anagnostopoulos

Maiano

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Social media and messaging apps have become major communication platforms. Multimedia contents promote improved user engagement and have thus become a very important communication tool. However, fake news and manipulated content can easily go viral, so, being able to verify the source of videos and images as well as to distinguish between native and downloaded content becomes essential. Most of the work performed so far on social media provenance has concentrated on images; in this paper, we propose a CNN architecture that analyzes video content to trace videos back to their social network of origin. The experiments demonstrate that stating platform provenance is possible for videos as well as images with very good accuracy.

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Social Network Identification Through Image Classification With CNN

Cited by 47 publications

References 25 publications

Smartphones Identification Through the Built-In Microphones With Convolutional Neural Network

Smartphones Identification Through the Built-In Microphones With Convolutional Neural Network

Enhancing Personalized Ads Using Interest Category Classification of SNS Users Based on Deep Neural Networks

Learning Double-Compression Video Fingerprints Left From Social-Media Platforms

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