Toward Reliable Mobile CrowdSensing Data Collection: Image Splicing Localization Overview

With the advancement of technology, the collection of data used in Intelligent Transportation Systems has become increasingly easy, notably with the emergence of the Mobile CrowdSensing paradigm. This paradigm could provide insights into traffic situation, road condition, pedestrians' behaviours, public transportation situation, and so on. Through the use of the powerful sensors in the mobile, different types of data can be generated, such as Gyroscope data, light sensors data, Magnetometer data, GPS data, Accelerometer data, videos, and images. However, the use of MCS raises issues of data reliability, as it involves the participation of several mobile owners with different 'levels' of trustworthiness. An important related issue is image forgery, i.e. the manipulation of images in order to deliberately provide misleading information, which constitutes a threat to the decision-makers of the MSC-based applications in Intelligent Transportation Systems. In this paper, an overview on image forgery is presented. We examine the workflows, approaches and techniques employed for image forgery detection and localization. Then, we provide a brief review of some image splicing localization techniques. Finally, we provide a comparative analysis of handcrafted feature extraction-based techniques and deep learning-based techniques. The aim of this paper is to draw attention to the image forgery problem that could threaten any Mobile CrowdSensing application dedicated to image collection, and to provide an overview of different existing techniques that could be used to overcome this problem.