“PhotoGuard,” developed by MIT CSAIL researchers, prevents unauthorized image manipulation, safeguarding authenticity in the era of advanced generative models.
Copyright: news.mit.edu – “Using AI to Protect Against AI Image Manipulation”
As we enter a new era where technologies powered by artificial intelligence can craft and manipulate images with a precision that blurs the line between reality and fabrication, the specter of misuse looms large. Recently, advanced generative models such as DALL-E and Midjourney, celebrated for their impressive precision and user-friendly interfaces, have made the production of hyper-realistic images relatively effortless. With the barriers of entry lowered, even inexperienced users can generate and manipulate high-quality images from simple text descriptions — ranging from innocent image alterations to malicious changes. Techniques like watermarking pose a promising solution, but misuse requires a preemptive (as opposed to only post hoc) measure.
In the quest to create such a new measure, researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) developed “PhotoGuard,” a technique that uses perturbations — minuscule alterations in pixel values invisible to the human eye but detectable by computer models — that effectively disrupt the model’s ability to manipulate the image.
PhotoGuard uses two different “attack” methods to generate these perturbations. The more straightforward “encoder” attack targets the image’s latent representation in the AI model, causing the model to perceive the image as a random entity. The more sophisticated “diffusion” one defines a target image and optimizes the perturbations to make the final image resemble the target as closely as possible.
“Consider the possibility of fraudulent propagation of fake catastrophic events, like an explosion at a significant landmark. This deception can manipulate market trends and public sentiment, but the risks are not limited to the public sphere. Personal images can be inappropriately altered and used for blackmail, resulting in significant financial implications when executed on a large scale,” says Hadi Salman, an MIT graduate student in electrical engineering and computer science (EECS), affiliate of MIT CSAIL, and lead author of a new paper about PhotoGuard.
“In more extreme scenarios, these models could simulate voices and images for staging false crimes, inflicting psychological distress and financial loss. The swift nature of these actions compounds the problem. Even when the deception is eventually uncovered, the damage — whether reputational, emotional, or financial — has often already happened. This is a reality for victims at all levels, from individuals bullied at school to society-wide manipulation.”[…]
Thank you for reading this post, don't forget to subscribe to our AI NAVIGATOR!
Read more: www.news.mit.edu