Legal conundrums are also emerging regarding where to put the responsibility for the possibly damaging fallout of fake content spreading. As of 2022, all of the major social network platforms are struggling to filter manipulated data, and so avoid that such fake content, often directed to the most vulnerable users, could “go viral”. Sometimes this is done for malevolent purposes, such as political or commercial ones. On the other hand, user-friendly, advanced image editing software, both commercial like Adobe Photoshop, and free and open source like GIMP, not to mention smartphone-based apps that can apply basic image manipulations on the fly, Footnote 1 are widely available to everyone.Īll these factors have contributed to the spread of fake or forged images and videos, in which the semantic content is significantly altered. These latter, in particular, include Web platforms, e.g., social networks such as Instagram and forums like Reddit, that allow the almost instantaneous spreading of user generated images and video. Massive sharing of visual content is enabled by a variety of digital technologies, such as effective compression methods, fast networks, and specially designed user applications. Indeed, a great amount of everyday facts are documented through the use of smartphones, even by professionals. The worldwide spread of smart devices, which integrate increasing quality cameras and image processing tools and “apps”, the ubiquity of desktop computers, and the fact that all these devices are almost permanently connected with each other and to remotely located data servers through the Internet, have given ordinary people the possibility to collect, store, and process an enormous quantity of digital visual data on a scale just until recently quite unthinkable.Īs a consequence, images and videos are often shared and considered as information sources in several different contexts. Building upon this analysis, we conclude by addressing possible future research trends and directions, in both deep learning architectural and evaluation approaches, and dataset building for easy methods comparison. We also discuss and compare (where possible) their performance. We discuss the key-aspects of these methods, while also describing the datasets on which they are trained and validated. This survey is especially timely because deep learning powered techniques appear to be the most relevant right now, since they give the best overall performances on the available benchmark datasets. DeepFake generated content is also addressed insofar as its application is aimed at images, achieving the same effect as splicing. In this paper, we conduct a survey of some of the most recent image forgery detection methods that are specifically designed upon Deep Learning (DL) techniques, focusing on commonly found copy-move and splicing attacks. A lot of different approaches have been proposed in order to assess the authenticity of an image and in some cases to localize the altered (forged) areas. NET 7.0 and must be manually compiled for use by the user.In the last years, due to the availability and easy of use of image editing tools, a large amount of fake and altered images have been produced and spread through the media and the Web. The 2.3 branch is maintained by Kostya and is more ahead than master in some ways, but behind in others. NET 6.0 and is automatically compiled for use by GitHub Actions. The master branch is maintained by Yunivers and is deemed more stable than the 2.3 branch. CTFAK 2.0 is currently split between 2 branches the master branch, and the 2.3 branch. With CTFAK 2.0's plugin system, it's easy for anyone to make a plugin compatible with CTFAK 2.0 which allows you to do anything with the data read by CTFAK 2.0 including, but not limited to, converting the data to other game engines, programming your own dumping method to suit however you want to organize your data, or messing with the outputted data by modifying the FTDecompile plugin. CTFAK 2.0 (Standing for ClickTeam Fusion Army Knife 2.0) is a tool developed by Kostya with help from Yunivers which can be used to either decompile or dump assets of games made with the Clickteam Fusion 2.5 game engine. CTFAK2.0 has no bugs, it has no vulnerabilities, it has a Strong Copyleft License and it has low support.
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