Jocelin Rosales Corripio

Smartphone image clustering

Every day the use of images from mobile devices as evidence in legal proceedings is more usual and common.Image source acquisition identification is a branch of digital forensic analysis.We use a combination of hierarchical and flat clustering and the use of Sensor Pattern Noise for source identification.We make a series of experiments which emulate similar situations to those that may occur in reality. Every day the use of images from mobile devices as evidence in legal proceedings is more usual and common. Therefore, forensic analysis of mobile device images takes on special importance. This paper explores the branch of forensic analysis which is based on the identification of the source, specifically on the grouping or clustering of images according to their source acquisition. In contrast with other state of the art techniques for source identification, hierarchical clustering does not involve a priori knowledge of the number of images or devices to be identified or training data for a future classification stage. That is, a grouping by classes with all the input images is performed. The proposal is based on the combination of hierarchical and flat clustering and the use of Sensor Pattern Noise (SPN). There has been a series of experiments which emulate similar situations to those that may occur in reality to test the robustness and reliability of the results of the technique. The results are satisfactory in all the experiments, obtaining high rates of success.

Image source acquisition identification of mobile devices based on the use of features

Nowadays, forensic analysis of digital images is especially important, given the frequent use of digital cameras in mobile devices. The identification of the device type or the make and model of image source are two important branches of forensic analysis of digital images. In this paper we have addressed both of these, with an approach based on different types of image features and the classification using support vector machines. The study has mainly focused on images created with mobile devices and as a result, the techniques and features have been adapted or created for this purpose. There have been a total of 36 experiments classified into 5 sets, in order to test different configurations of the techniques. In the configuration of the experiments, the future use of the technique by the forensic analyst in real situations to create experiments with high technical requirements was taken into account, amongst other things.

A PRNU-based counter-forensic method to manipulate smartphone image source identification techniques

The increased diffusion of digital images generated by mobile devices through social networks, personal and professional communications, etc. is self-evident. This creates potential problems because some of these images may be used as supporting evidence for different criminal cases. In this paper, algorithms are proposed based on sensor noise and wavelet transforms which can alter the information which is usually employed to find the source of an image, and forge it so that it could point to a different, unrelated device. In the state of art we will show that there are already some algorithms capable of carrying out these manipulations, but they generally need much more and more complex data than our proposal. They also generally need physical access to the camera whose generated images you want to tamper. Our proposal algorithm to destruct the image identifiable data, only needs the picture which will be anonymized. Also, our proposal to forge the image identifiable data only needs a set of photos from the attacker camera, and the picture to be tampered. In particular, it does not need access to the camera that will be falsely linked to the picture. These scenarios are the most common and realistic. The algorithms proposed will help to strengthen existing techniques and develop new forensic approaches for mobile image source identification that will be more robust against attacks. This research presents a Counter-Forensic method based on sensor noise and wavelet transform.The paper proposes two algorithms, one to destroy of image identity and another to forge of a given image identity.The aim of destroy of image identity algorithm is anonymize an image.The aim of forge of a given image identity algorithm is forgery the source of acquisition of an image.

Theia: a tool for the forensic analysis of mobile devices pictures

Currently the number of cameras embedded in mobile devices is growing at an unprecedented rate. Additionally, the quality and performance of these mobile cameras is steadily improving, and is closing in on that of classical digital cameras. This scenario makes the forensic analysis of images taken with mobile cameras increasingly important and necessary. Among the various branches of forensic analysis, this paper focuses on the reliable acquisition of the make and model of the mobile camera that produced a given image. For this we have developed a technique based on exchangeable image file format (Exif) metadata analysis, allowing us in certain cases to obtain both the make and model with which the photo was taken. This comes with considerable analysis of whether this metadata information could be trusted, and with additional tools that can help in discovering image manipulation. These and other capabilities have been integrated into a new tool we have developed called Theia, that also offers many other advantages to the forensic analyst that has to mass process and analyze thousands of images in the fastest and most forensically sound way. To that end, we have also incorporated various complex functions that greatly help the forensic analyst, such as different types of advanced queries on Exif metadata information of large sets of images, and advanced geopositioning capabilities.