Digital forensic analysis for source video identification: A survey

“…[20] is used as a benchmark dataset comprising of more than 1000 videos which is used as a a baseline for basic image forensic operations including identifying and segmenting fabricated images. In 2018, Korshunov et al [21] presented a public video dataset of low and high quality video sequences comprised of 620 deepfake videos developed using GAN based method which is obtained from VidTIMIT dataset 6 .…”

Section: Related Workmentioning

confidence: 99%

“…The use of footage from multiple smartphones is a distinctive feature in this dataset, as other datasets use different single-lens reflex cameras and other closed-circuit television camera, which are difficult to mobilize and expensive to purchase compared to smartphones [5]. Our knowledge of the source of the videos covers a wide range of methods, from Photo Response Non Uniformity (PRNU) methods to Deep Learning methods [6]. In the present scenario, smartphones are replacing digital singlelens reflex (DSLR) cameras with high-quality pictures and videos in addition to cloud backup facilities on smartphones for convenient storage.…”

Section: Introductionmentioning

confidence: 99%

A New Dataset for Forged Smartphone Videos Detection: Description and Analysis

et al. 2023

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The advancement of Internet technology has significantly impacted daily life, which is influenced by digital videos taken with smartphones as the most popular type of multimedia. These digital videos are extensively sent through various social media websites such as WhatsApp, Instagram, Facebook, Twitter, and YouTube. The development of intelligent and simple editing tools has favoured the transformation of multimedia content on the Internet. As a result, these digital videos' credibility, reliability, and integrity have become critical concerns. This paper presents a video forgery (Copy-move forgery) dataset in which 250 original videos are manipulated mainly by two forgery techniques: Insertion and Deletion. Inserting transparent objects into the original video without raising suspicion is one type of manipulation performed. Another type of forgery presented on the dataset is the removal of objects from the original video without notifying the viewers. The videos were collected from five different mobile devices, namely, IPhone 8 Plus, Nokia 5.4, Samsung A50, Xiomi Redmi Note 9 Pro and Huawei Y9-1. The forged videos were created using a popular video editing software called Adobe After Effects as well as usage of other software such as Adobe Photoshop and AfterCodecs. Since the source of the videos is known, PRNUbased methods can be suitable for applying to the dataset. Experiments were performed using classical and deep learning methods. The results are recorded and discussed in detail, showing that improvements are essential for the dataset. Furthermore, the forged videos of this dataset are comparatively large when compared to other datasets that performed copy-move forgery.

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“…There are fewer examples of using PRNU-based SCI for videos, a survey of which can be found in [ 22 ]. For example, in [ 23 ], Al-Athamneh et al simplified this application by using only the green channel to compute the PRNU, due to the greater importance attached by image sensor designers to the green channel (double the number of samples from the red and blue channels); the authors also claimed that the green channel has stronger noise.…”

Section: Introductionmentioning

confidence: 99%

A Stress Test for Robustness of Photo Response Nonuniformity (Camera Sensor Fingerprint) Identification on Smartphones

Martín-Rodríguez

Isasi-de-Vicente

Fernandez-Barciela

2023

Sensors

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In the field of forensic imaging, it is important to be able to extract a camera fingerprint from one or a small set of images known to have been taken by the same camera (or image sensor). Note that we are using the word fingerprint because it is a piece of information extracted from images that can be used to identify an individual source camera. This technique is very important for certain security and digital forensic situations. Camera fingerprint is based on a certain kind of random noise present in all image sensors that is due to manufacturing imperfections and is, thus, unique and impossible to avoid. Photo response nonuniformity (PRNU) has become the most widely used method for source camera identification (SCI). In this paper, a set of attacks is designed and applied to a PRNU-based SCI system, and the success of each method is systematically assessed both in the case of still images and in the case of video. An attack method is defined as any processing that minimally alters image quality and is designed to fool PRNU detectors or, in general, any camera fingerprint detector. The success of an attack is assessed as the increment in the error rate of the SCI system. The PRNU-based SCI system was taken from an outstanding reference that is publicly available. Among the results of this work, the following are remarkable: the use of a systematic and extensive procedure to test SCI methods, very thorough testing of PRNU with more than 2000 test images, and the finding of some very effective attacks on PRNU-based SCI.

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Digital forensic analysis for source video identification: A survey

Cited by 19 publications

References 76 publications

Interpol review of forensic video analysis, 2019–2022

Interpol review of forensic video analysis, 2019–2022

A New Dataset for Forged Smartphone Videos Detection: Description and Analysis

A Stress Test for Robustness of Photo Response Nonuniformity (Camera Sensor Fingerprint) Identification on Smartphones

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