<story><title>A new way to detect ‘deepfake’ picture editing</title><url>https://www.lightbluetouchpaper.org/2021/06/07/a-new-way-to-detect-deepfake-picture-editing/</url></story><parent_chain></parent_chain><comment><author>Etheryte</author><text>It&amp;#x27;s hard to see this as much anything but snake oil. While the technique allegedly allows you to target multiple models, it still requires you to know what adversarial model you&amp;#x27;re targeting before the fact. If the adversary sees there&amp;#x27;s visible artifacts they&amp;#x27;ll just change a few parameters or use a different model and be good to go none the less. In short, while this might be academically interesting, I can&amp;#x27;t help but feel that this is a futile field to work in. A good way to see why is to look at it this way: a skilled human can manually manipulate an image so that it isn&amp;#x27;t possible to tell that the image has been manipulated. It is then a matter of teaching the model to do the same, which might take time or be computationally prohibitive for now, but eventually it will be done.</text></comment>
<story><title>A new way to detect ‘deepfake’ picture editing</title><url>https://www.lightbluetouchpaper.org/2021/06/07/a-new-way-to-detect-deepfake-picture-editing/</url></story><parent_chain></parent_chain><comment><author>d--b</author><text>Adversial techniques are specific to the model used, no?&lt;p&gt;You&amp;#x27;d need to know all the models that one might use to tamper with a picture and modify the image in such a way that it screws them all.&lt;p&gt;And the first thing that will happen is that people are going to train their models with these new &amp;#x27;tamper-protected&amp;#x27; images...</text></comment>