With the rapid progress of recent years, techniques that generate and manipulate multimedia content can now guarantee a very advanced level of realism. The boundary between real and synthetic media has become very thin. On the one hand, this opens the door to a series of exciting applications in different fields such as creative arts, advertising, film production, video games. On the other hand, it poses enormous security threats. Software packages freely available on the web allow any individual, without special skills, to create very realistic fake images and videos. So-called deepfakes can be used to manipulate public opinion during elections, commit fraud, discredit or blackmail people. Potential abuses are limited only by human imagination. Therefore, there is an urgent need for automated tools capable of detecting false multimedia content and avoiding the spread of dangerous false information. 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. Special emphasis will be placed on the emerging phenomenon of deepfakes and, from the point of view of the forensic analyst, on modern data-driven forensic methods. The analysis will help to highlight the limits of current forensic tools, the most relevant issues, the upcoming challenges, and suggest future directions for research.

Media Forensics and DeepFakes: an overview

Opportunities and challenges of deep learning methods for electrocardiogram data: A systematic review.

We introduce a novel deep neural architecture for image copy-move forgery detection (CMFD), code-named BusterNet. Unlike previous efforts, BusterNet is a pure, end-to-end trainable, deep neural network solution. It features a two-branch architecture followed by a fusion module. The two branches localize potential manipulation regions via visual artifacts and copy-move regions via visual similarities, respectively. To the best of our knowledge, this is the first CMFD algorithm with discernibility to localize source/target regions. We also propose simple schemes for synthesizing large-scale CMFD samples using out-of-domain datasets, and stage-wise strategies for effective BusterNet training. Our extensive studies demonstrate that BusterNet outperforms state-of-the-art copy-move detection algorithms by a large margin on the two publicly available datasets, CASIA and CoMoFoD, and that it is robust against various known attacks.

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BusterNet: Detecting Copy-Move Image Forgery with Source/Target Localization

Copy-move forgery detection

Image fusion meets deep learning: A survey and perspective

A novel copy–move forgery detection scheme using adaptive oversegmentation and feature point matching is proposed in this paper. The proposed scheme integrates both block-based and keypoint-based forgery detection methods. First, the proposed adaptive oversegmentation algorithm segments the host image into nonoverlapping and irregular blocks adaptively. Then, the feature points are extracted from each block as block features, and the block features are matched with one another to locate the labeled feature points; this procedure can approximately indicate the suspected forgery regions. To detect the forgery regions more accurately, we propose the forgery region extraction algorithm, which replaces the feature points with small superpixels as feature blocks and then merges the neighboring blocks that have similar local color features into the feature blocks to generate the merged regions. Finally, it applies the morphological operation to the merged regions to generate the detected forgery regions. The experimental results indicate that the proposed copy–move forgery detection scheme can achieve much better detection results even under various challenging conditions compared with the existing state-of-the-art copy–move forgery detection methods.

Image Forgery Detection Using Adaptive Oversegmentation and Feature Point Matching

Heart sounds classification using a novel 1-D convolutional neural network with extremely low parameter consumption

Detecting a splicing forgery image and then locating the forgery regions is a challenging task. Some traditional feature extraction methods and convolutional neural network (CNN)-based detection methods have been proposed to finish this task by exploring the differences of image attributes between the un-tampered and tampered regions in a image. However, the performance of the existing detection methods is unsatisfactory. In this paper, we propose a ringed residual U-Net (RRU-Net) for image splicing forgery detection. The proposed RRU-Net is an end-to-end image essence attribute segmentation network, which is independent of human visual system, it can accomplish the forgery detection without any preprocessing and post-processing. The core idea of the proposed RRU-Net is to strengthen the learning way of CNN, which is inspired by the recall and the consolidation mechanism of the human brain and implemented by the propagation and the feedback process of the residual in CNN. The residual propagation recalls the input feature information to solve the gradient degradation problem in the deeper network; the residual feedback consolidates the input feature information to make the differences of image attributes between the un-tampered and tampered regions be more obvious. Experimental results show that the proposed detection method can achieve a promising result compared with the state-of-the-art splicing forgery detection methods.

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Xiuli Bi

Papers

Image Forgery Detection Using Adaptive Oversegmentation and Feature Point Matching

Heart sounds classification using a novel 1-D convolutional neural network with extremely low parameter consumption

RRU-Net: The Ringed Residual U-Net for Image Splicing Forgery Detection

Fractional discrete Tchebyshev moments and their applications in image encryption and watermarking

Scaling and rotation invariant analysis approach to object recognition based on Radon and Fourier-Mellin transforms