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MIT’s “PhotoGuard” Protects Images From Unauthorized AI Edits
The technology invisibly alters select pixels to throw off algorithmic AI models.
As AI continues to develop rapidly, chatbots are gaining the power to create and manipulate images, with Shutterstock and Adobe currently leading the way. Despite the obvious power of AI algorithms, the technology has a few pitfalls, one of which is the unauthorized manipulation of copyrighted artwork and images.
MIT CSAIL thinks it has the answer to this growing problem in the form of PhotoGuard, a new technique that alters select pixels in an image to disrupt AI’s ability to understand what the image is.
The altered pixels are known as “perturbations” and are invisible to the human eye but easily seen by AI bots as they scan the color and position of every pixel in an image. Any edits AI tries to make to a protected image will also apply to the fake pixels, resulting in an unrealistic or broken final image, thanks to PhotoGuard.
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“The encoder attack makes the model think that the input image is some other image,” explained MIT student and lead author of the paper, Hadi Salman. “Whereas the diffusion attack forces the diffusion model to make edits towards some target image”. The technique sounds complex but could potentially stop malicious actors from reverse engineering protected images by adding minor edits to circumvent copyright.
“A collaborative approach involving model developers, social media platforms, and policymakers presents a robust defense against unauthorized image manipulation. Working on this pressing issue is of paramount importance today,” Salman said in a recent press release. “And while I am glad to contribute towards this solution, much work is needed to make this protection practical. Companies that develop these models need to invest in engineering robust immunizations against the possible threats posed by these AI tools”.
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Nano Banana 2 Arrives In MENA For Google Gemini Users
Google brings its latest image model to Gemini and Search, adding 4K output and tighter text control for regional users.
Google has opened access to Nano Banana 2 across the Middle East and North Africa, pushing its newest image model into everyday tools rather than keeping it inside the exclusive (and expensive) Pro tier.
The rollout spans the Google Gemini desktop and mobile apps, and extends to Google Search through Lens and AI Mode. Developers can also test it in preview via AI Studio and the Gemini API.
Nano Banana 2 runs on Gemini Flash, Google’s fast inference layer. The focus is speed, but also control. Users can export visuals from 512px up to 4K, adjusting aspect ratios for everything from vertical social posts to widescreen displays.
The model maintains character likeness across up to five figures and preserves fidelity for as many as 14 objects within a single workflow. This enables visual continuity across scenes, iterations, or edits — supporting projects like short films, storyboards, and multi-scene narratives. Text rendering has also been improved, delivering legible typography in mockups and greeting cards, with built-in translation and localization directly within images.
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Under the hood, the system taps Gemini’s broader knowledge base and pulls in real-time information and imagery from web search to render specific subjects more accurately. Lighting and fine detail have been upgraded, without slowing output.
By embedding the model inside Gemini and Search, Google is normalizing advanced image generation for a mass audience. In MENA, where startups and marketing teams are leaning heavily on AI to scale content across languages and borders, that shift lands at a practical moment.
The move also folds creative tooling deeper into search itself, so that image generation is no longer a separate workflow. It now sits right next to the query box.
Nano Banana 2 Arrives In MENA For Google Gemini Users
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