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Open AccessJournal ArticleDOI

An overview on video forensics

TLDR
The paper aims at providing an overview of the existing video processing techniques, considering all the possible alterations that can be operated on a single signal and also the possibility of identifying the traces that could reveal important information about its origin and use.
Abstract
Validating a given multimedia content is nowadays quite a hard task because of the huge amount of possible alterations that could have been operated on it. In order to face this problem, image and video experts have proposed a wide set of solutions to reconstruct the processing history of a given multimedia signal. These strategies rely on the fact that non-reversible operations applied to a signal leave some traces ("footprints") that can be identified and classified in order to reconstruct the possible alterations that have been operated on the original source. These solutions permit also to identify which source generated a specific image or video content given some device-related peculiarities. The paper aims at providing an overview of the existing video processing techniques, considering all the possible alterations that can be operated on a single signal and also the possibility of identifying the traces that could reveal important information about its origin and use.

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Proceedings ArticleDOI

MesoNet: a Compact Facial Video Forgery Detection Network.

TL;DR: A method to automatically and efficiently detect face tampering in videos, and particularly focuses on two recent techniques used to generate hyper-realistic forged videos: Deepfake and Face2Face.
Journal ArticleDOI

DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection

TL;DR: This survey provides a thorough review of techniques for manipulating face images including DeepFake methods, and methods to detect such manipulations, with special attention to the latest generation of DeepFakes.
Posted Content

Media Forensics and DeepFakes: an overview

TL;DR: This review paper aims to present an analysis of the methods for visual media integrity verification, that is, the detection of manipulated images and videos, with special emphasis on the emerging phenomenon of deepfakes, fake media created through deep learning tools, and on modern data-driven forensic methods to fight them.
Proceedings ArticleDOI

A deep neural network for image quality assessment

TL;DR: This paper presents a no reference image quality assessment (IQA) method based on a deep convolutional neural network (CNN) that takes unpreprocessed image patches as an input and estimates the quality without employing any domain knowledge.
Proceedings ArticleDOI

Local tampering detection in video sequences

TL;DR: The analysis of the footprints left when tampering with a video sequence is presented, and a detection algorithm is proposed that allows a forensic analyst to reveal video forgeries and localize them in the spatio-temporal domain.
References
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Journal ArticleDOI

The JPEG still picture compression standard

TL;DR: The Baseline method has been by far the most widely implemented JPEG method to date, and is sufficient in its own right for a large number of applications.
Journal ArticleDOI

The JPEG still picture compression standard

TL;DR: The author provides an overview of the JPEG standard, and focuses in detail on the Baseline method, which has been by far the most widely implemented JPEG method to date, and is sufficient in its own right for a large number of applications.
Journal ArticleDOI

Digital camera identification from sensor pattern noise

TL;DR: A new method is proposed for the problem of digital camera identification from its images based on the sensor's pattern noise, which serves as a unique identification fingerprint for each camera under investigation by averaging the noise obtained from multiple images using a denoising filter.
Journal ArticleDOI

Determining Image Origin and Integrity Using Sensor Noise

TL;DR: A unified framework for identifying the source digital camera from its images and for revealing digitally altered images using photo-response nonuniformity noise (PRNU), which is a unique stochastic fingerprint of imaging sensors is provided.