3

Not necessarily it depends on the function of the problem space for both the GANs. A real world example: a batter's reaction time and a pitchers max speed are actual bounded values based on genetics and physics. If the max speed a pitcher can pitch is greater than the max reaction time a human needs to effectively hit against them they will permanently be ...


2

They can indeed. Although generally they are kept to images because at the moment, they are the best at that, but not the best in other areas that you might consider. GANs can be used for audio generation, with many examples such as GANsynth and GAN voice generation. But each of these tasks are outperformed by other methods. With music generation, WaveNet is ...


2

In general, deepfakes rely on advanced context-aware digital signal manipulations - usually image, video or audio - that allow for very natural looking modifications of content that previously have been costly or near impossible to produce in high quality. The AI models, often based on generative adversarial networks (GANs), style transfer, pose estimation ...


2

Digital Media Forensics (DMF) field aims to develop technologies for the automated assessment of the integrity of an image or video, so DMF is the field you are looking for. There are several approaches in DMF: for example, those based on machine learning (ML) techniques, in particular, convolutional neural networks (CNNs). For example, in the paper ...


2

Yes. There are services that provide free environment to run jupyter notebooks for research purposes (with GPU included, which is crucial for neural networks) - such as Google Colaboratory and Kaggle Kernels. Although they limit how long your computation may run (12 and 6 hours accordingly), which adds some difficulties to the process, although I think it is ...


2

I think this game will go pretty crazy, because, at some point, the generator AI will be able to generate absolutely perfect images. Actually, no, just perfect enough that no AI can be sure whether they are real or fake. So, I think the AI war will go onto more than the image, the detector AI will probably evolve to analyze whether this video is logically ...


1

As mshlis begins to touch on, yes we can. However, it will be an unending war. There are quite a few reasons for this. For one, the problem itself is not simple. There are many different 'versions' of the deepfakes framework out in the wild at this point, any algorithm you create to try and spot them would have to work for all of the different iterations. ...


1

Take a look at using AWS You can spin up an instance with as much processing power as you need and by using their pre-built images it will already be preconfigured with a lot of the packages etc you might need for any kind of ML. I see you wanted to use DeepFaceLab which I guess requires some kind of GUI so unsure if this is suitable for your requirements ...


1

It is possible! Here is an article by Adobe where they explain how they do it: https://theblog.adobe.com/spotting-image-manipulation-ai/ The algorithm for this would almost certainly be a Convolutional Neural Net trained on a dataset of real and manipulated images (labeled as such).


1

I think context is important here. Using tactics like those used by Scotland Yard for over a century is probably the best way. Establishing alibis, realistic time lines, motives. For a legal setting, it would be possible to prove these images were fake using methods like this. From an I.T. perspective, it may be possible to pinpoint an origin for these ...


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