![]() Making watermarks less removable gives greater security and profit protection, while other images could be treated so that any future manipulation, such as object removal, becomes easier to detect. This research, which is supported by CIFAR (through a Canada CIFAR AI Chair), EPSRC, Apple, Bosch Research Foundation, NSERC, and Microsoft, brings new ways for creators, companies, and agencies to better protect their digital assets in the future. How can one stop it? We developed Markpainting to make it harder. Which of the images was inpainted? Modern ML makes it easy to manipulate media, helping misinformation campaigns. In a twist on adversarial ML, prevent malicious applications of “inpainting” (filling in a missing portion of an image) by adding an adversarial example perturbation to an image. ![]() This tool, described in detail by the team’s paper, uses “adversarial machine-learning techniques to fool the inpainter into making its edits evident to the naked eye”, whereby the “image owner can modify their image in subtle ways which are not themselves very visible, but will sabotage any attempt to inpaint it by adding visible information determined in advance by the markpainter.” It is a novel tool that can be used as “a manipulation alarm that becomes visible in the event of inpainting.” To make inpainting abuse more difficult, a team of researchers - David Khachaturov, Ilia Shumailov, Yiren Zhao, Nicolas Papernot, and Ross Anderson - have put together an initiative, called “ Markpainting,” as spotted by Light Blue Touchpaper. Manipulating images with malicious intent can cause not only profit loss from image theft by removing watermarks or other visual copyright identifying factors, but it can also lead to the spread misinformation in the case of its ability to remove a person from a crime scene photo, scam people or businesses, even destabilize politics in a case earlier reported by PetaPixel. This type of technology has greatly developed in recent years, with the notable example of NVIDIA’s AI-powered “Content-Aware Fill”, which goes a step further than Photoshop’s already advanced tools. Although it is generally used as a tool among creatives to “clean up” the image for a more fine-tuned result, this technology can also used for malicious intentions, such as removing watermarks, reconstructing the reality by removing people or certain objects in the photos, adding false information, and more. Using it are difficult for adversaries to remove.Inpainting - also known as “Content-Aware Fill” for Photoshop users - is a method that uses machine-learning models to reconstruct missing pieces of an image or to remove unwanted objects. Markpainting technique is transferable to models that have differentĪrchitectures or were trained on different datasets, so watermarks created Watermark if the editor had been trying to remove it. We find that we can target multiple different models That any attempt to edit it using that model will add arbitrary visible Owner with access to an inpainting model can augment their image in such a way To manipulate it using our markpainting technique. Recently, inpainting startedīeing used for watermark removal, raising concerns. Modeling and used to populate masked or missing pieces in an image it has wideĪpplications in picture editing and retouching. Authors: David Khachaturov, Ilia Shumailov, Yiren Zhao, Nicolas Papernot, Ross Anderson Download PDF Abstract: Inpainting is a learned interpolation technique that is based on generative
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