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Photoguard

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technology2 years ago

Artists Embrace New Tools to Safeguard Their Work from AI's Influence

Artists and researchers are developing new tools to protect art and images from the grasp of artificial intelligence (AI). Glaze, developed by computer scientists at the University of Chicago, uses pixel-level tweaks to make artworks imperceptible to AI models, preventing them from understanding the images. Another tool, PhotoGuard, developed by researchers at MIT, puts an invisible "immunization" over images, making them resistant to manipulation by AI models. These tools aim to protect artists' unique works and prevent the theft and misuse of images online. However, artists and researchers emphasize the need for regulation to address the broader risks posed by AI-generated images and deepfakes.

via CNN|
#ai#art#glaze
technology2 years ago

"Steg.AI: Safeguarding Images with Clever AI Defense Against Manipulation"

Researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) have developed a technique called "PhotoGuard" to protect against AI image manipulation. PhotoGuard uses perturbations, invisible to the human eye but detectable by computer models, to disrupt an AI model's ability to manipulate an image. The technique includes two attack methods: an "encoder" attack that alters the image's latent representation, and a "diffusion" attack that optimizes perturbations to resemble a target image. By introducing these perturbations, the original image remains visually unaltered for humans but is protected against unauthorized edits by AI models. The researchers emphasize the need for collaboration among model developers, social media platforms, and policymakers to combat image manipulation.

via MIT News|
#ai#artificial-intelligence#image-manipulation
technology2 years ago

"MIT's 'PhotoGuard': Safeguarding Your Images Against AI Manipulation"

MIT CSAIL has developed a technique called "PhotoGuard" to protect images from malicious AI edits. The technique introduces invisible "perturbations" to disrupt an AI's understanding of the image, making it difficult for the AI to manipulate or steal the artwork. The method includes an "encoder" attack that alters pixels to confuse the AI's perception of the image and a "diffusion" attack that camouflages the image as a different one. While not foolproof, the technique highlights the need for collaboration between model developers, social media platforms, and policymakers to defend against unauthorized image manipulation.

via Engadget|
#ai#algorithmic-models#image-protection