Classification and Clustering of Shared Images on Social Networks and User Profile Linking

The ever increasing prevalence of smartphones and the popularity of social network platforms have facilitated instant sharing of multimedia content through social networks. However, the ease in taking and sharing photos and videos through social networks also allows privacy-intrusive and illegal content to be widely distributed. As such, images captured and shared by users on their profiles are considered as significant digital evidence for social network data analysis. The Sensor Pattern Noise (SPN) caused by camera sensor imperfections during the manufacturing process mainly consists of the Photo-Response Non-Uniformity (PRNU) noise that can be extracted from taken images without hacking the device. It has been proven to be an effective and robust device fingerprint that can be used for different important digital image forensic tasks, such as image forgery detection, source device identification and device linking. Particularly, by fingerprinting the camera sources captured a set of shared images on social networks, User Profile Linking (UPL) can be performed on social network platforms. The aim of this thesis is to present effective and robust methods and algorithms for better fulfilling shared image analysis based on SPN. We propose clustering and classification based methods to achieve Smartphone Identification (SI) and UPL tasks, given a set of images captured by a known number of smartphones and shared on a set of known user profiles. The important outcome of the proposed methods is UPL across different social networks where the clustered images from one social network are applied to fingerprint the related smartphones and link user profiles on the other social network. Also, we propose two methods for large-scale image clustering of different types of the shared images by users, without prior knowledge about the types and number of the smartphones

User profiles’ image clustering for digital investigations

Sharing images on Social Network (SN) platforms is one of the most widespread behaviors which may cause privacy-intrusive and illegal content to be widely distributed. Clustering the images shared through SN platforms according to the acquisition cameras embedded in smartphones is regarded as a significant task in forensic investigations of cybercrimes. The Sensor Pattern Noise (SPN) caused by camera sensor imperfections due to the manufacturing process has been proved to be an effective and robust camera fingerprint that can be used for several tasks, such as digital evidence analysis, smartphone fingerprinting and user profile linking as well. Clustering the images uploaded by users on their profiles is a way of fingerprinting the camera sources and it is considered a challenging task since users may upload different types of images, i.e., the images taken by users’ smartphones (taken images) and single images from different sources, cropped images, or generic images from the Web (shared images). The shared images make a perturbation in the clustering task, as they do not usually present sufficient characteristics of SPN of their related sources. Moreover, they are not directly referable to the user’s device so they have to be detected and removed from the clustering process. In this paper, we propose a user profiles’ image clustering method without prior knowledge about the type and number of the camera sources. The hierarchical graph-based method clusters both types of images, taken images and shared images. The strengths of our method include overcoming large-scale image datasets, the presence of shared images that perturb the clustering process and the loss of image details caused by the process of content compression on SN platforms. The method is evaluated on the VISION dataset, which is a public benchmark including images from 35 smartphones. The dataset is perturbed by 3000 images, simulating the shared images from different sources except for users’ smartphones. Experimental results confirm the robustness of the proposed method against perturbed datasets and its effectiveness in the image clustering

Shared Images and Camera Fingerprinting May Lead to Privacy Issues

Social networks have become an indispensable part of everyday life by providing users with different types of interaction. However, sharing different types of data, such as text, image, video and etc., on social networks, gives rise to user privacy concerns and risks, while the user is not aware of that. In this chapter, we show how the images shared by users can be applied to fingerprint the acquisition devices and link user profiles on social networks

Shared Images and Camera Fingerprinting May Lead to Privacy Issues.

Social networks have become an indispensable part of everyday life by providing users with different types of interaction. However, sharing different types of data, such as text, image, video and etc., on social networks, gives rise to user privacy concerns and risks, while the user is not aware of that. In this chapter, we show how the images shared by users can be applied to fingerprint the acquisition devices and link user profiles on social networks

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

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

An Interpretable Hand-Crafted Feature-Based Model for Atrial Fibrillation Detection

International audienceAtrial Fibrillation (AF) is the most common type of cardiac arrhythmia. Early diagnosis of AF helps to improve therapy and prognosis. Machine Learning (ML) has been successfully applied to improve the effectiveness of Computer-Aided Diagnosis (CADx) systems for AF detection. Presenting an explanation for the decision made by an ML model is considerable from the cardiologists' point of view, which decreases the complexity of the ML model and can provide tangible information in their diagnosis. In this paper, a range of explanation techniques is applied to hand-crafted features based ML models for heart rhythm classification. We validate the impact of the techniques by applying feature selection and classification to the 2017 CinC/PhysioNet challenge dataset. The results show the effectiveness and efficiency of SHapley Additive exPlanations (SHAP) technique along with Random Forest (RF) for the classification of the Electrocardiogram (ECG) signals for AF detection with a mean F-score of 0.746 compared to 0.706 for a technique based on the same features based on a cascaded SVM approach. The study also highlights how this interpretable hand-crafted feature-based model can provide cardiologists with a more compact set of features and tangible information in their diagnosis

Hybrid clustering of shared images on social networks for digital forensics

Clustering the images shared through social network (SN) platforms according to the acquisition cameras embedded in smartphones is regarded as a significant task in forensic investigations of cybercrimes. The sensor pattern noise (SPN) caused by the camera sensor imperfections during the manufacturing process can be extracted from the images and used to fingerprint the smartphones. The process of content compression performed by the SNs causes loss of image details and weakens the SPN, making the clustering task even more challenging. In this paper, we present a hybrid algorithm capable of clustering the images captured and shared through SNs without prior knowledge about the types and number of the acquisition smartphones. The hybrid method exploits batch partitioning, image resizing, hierarchical and graph-based clustering approaches to cluster the images. Using Markov clustering, the hierarchical clustering is conducted in such a way that the representative clusters with a higher probability of belonging to the same camera are selected for merging, which accelerates the clustering. For merging the clusters, the adaptive threshold updated iteratively through the hybrid clustering is used, which results in more precise clusters even for images from the same model of smartphones. The results on the VISION dataset, including both native and shared images, prove the effectiveness and efficiency of the hybrid method in comparison with the state-of-the-art SPN-based image clustering algorithms