When you purchase through links in our articles, we may earn a small commission. While you can set the Gemini app to create images and videos without a visible watermark, hidden watermarks and metadata will remain. On a week when AI watermarking is a hot topic, Google has just announced that Gemini will let you drop the visible watermarks from created images and videos. Of course, "visible" is the key word here, with Google exec Josh Woodward explaining in a social post that invisible SynthID watermarks and metadata will remain embedded in created Gemini images. "We're striking a balance here between creative control and safety," Woodard wrote on X, adding that the option to drop the visible watermark is available for the Nano Banana image model, the Omni model for videos, and Lyria for generated music.

Google VP Josh Woodward just announced that users will be able to remove visible watermarks from AI-generated images and videos. A toggle will soon be available for the Nano Banana, Omni and Lyria models, which all use the familiar sparkle icon to denote AI. This kind of defeats the whole point of visible watermarks, as they exist to give people a fast way to spot AI-generated content out in the wild. However, users will still have slightly harder methods to identify this type of thing. This move will not impact the invisible SynthID watermark and C2PA metadata, as there are are no toggles to shut those off.

As AI advances, copyrighted content faces growing risk of unauthorized use, whether through model training or direct misuse. Building upon invisible adversarial perturbation, recent works developed copyright protections against specific AI techniques such as unauthorized personalization through DreamBooth that are misused. However, these methods offer only short-term security, as they require retraining whenever the underlying model architectures change. To establish long-term protection aiming at better robustness, we go beyond invisible perturbation, and propose a universal approach that embeds \textit{visible} watermarks that are \textit{hard-to-remove} into images. Grounded in a new probabilistic and inverse problem-based formulation, our framework maximizes the discrepancy between the \textit{optimal} reconstruction and the original content. We develop an effective and efficient approximation algorithm to circumvent a intractable bi-level optimization. Experimental results demonstrate superiority of our approach across diverse scenarios.

Diffusion Models (DMs) have shown remarkable capabilities in various image-generation tasks. However, there are growing concerns that DMs could be used to imitate unauthorized creations and thus raise copyright issues. To address this issue, we propose a novel framework that embeds personal watermarks in the generation of adversarial examples. Such examples can force DMs to generate images with visible watermarks and prevent DMs from imitating unauthorized images. We construct a generator based on conditional adversarial networks and design three losses (adversarial loss, GAN loss, and perturbation loss) to generate adversarial examples that have subtle perturbation but can effectively attack DMs to prevent copyright violations. Training a generator for a personal watermark by our method only requires 5-10 samples within 2-3 minutes, and once the generator is trained, it can generate adversarial examples with that watermark significantly fast (0.2s per image). We conduct extensive experiments in various conditional image-generation scenarios. Compared to existing methods that generate images with chaotic textures, our method adds visible watermarks on the generated images, which is a more straightforward way to indicate copyright violations. We also observe that our adversarial examples exhibit good transferability across unknown generative models. Therefore, this work provides a simple yet powerful way to protect copyright from DM-based imitation.

Microsoft has started a coalition of tech companies and media companies to develop a common standard for watermarking AI images, and the company has said it is researching new methods to track AI images. The company also places a small visible watermark in the corner of images generated by its AI tools. OpenAI, whose Dall-E image generator helped kick off the wave of interest in AI last year, also adds a visible watermark. AI researchers have suggested ways of embedding digital watermarks that the human eye can't see but can be identified by a computer.