Watermarking images is critical for tracking image provenance and proving ownership.

Watermarking images is critical for tracking image provenance and proving ownership. With the advent of generative models, such as stable diffusion, that can create fake but realistic images, watermarking has become particularly important to make human-created images reliably identifiable. Unfortunately, the very same stable diffusion technology can remove watermarks injected using existing methods.To address this problem, we present ZoDiac, which uses a pre-trained stable diffusion model to inject a watermark into the trainable latent space, resulting in watermarks that can be reliably detected in the latent vector even when attacked. We evaluate ZoDiac on three benchmarks, MS-COCO, DiffusionDB, and WikiArt, and find that ZoDiac is robust against state-of-the-art watermark attacks, with a watermark detection rate above 98% and a false positive rate below 6.4%, outperforming state-of-the-art watermarking methods. We hypothesize that the reciprocating denoising process in diffusion models may inherently enhance the robustness of the watermark when faced with strong attacks and validate the hypothesis. Our research demonstrates that stable diffusion is a promising approach to robust watermarking, able to withstand even stable-diffusion-based attack methods.

Watermarking images is critical for tracking image provenance and proving ownership. With the advent of generative models, such as stable diffusion, that can create fake but realistic images, watermarking has become particularly important to make human-created images reliably identifiable. Unfortunately, the very same stable diffusion technology can remove watermarks injected using existing methods.To address this problem, we present ZoDiac, which uses a pre-trained stable diffusion model to inject a watermark into the trainable latent space, resulting in watermarks that can be reliably detected in the latent vector even when attacked. We evaluate ZoDiac on three benchmarks, MS-COCO, DiffusionDB, and WikiArt, and find that ZoDiac is robust against state-of-the-art watermark attacks, with a watermark detection rate above 98% and a false positive rate below 6.4%, outperforming state-of-the-art watermarking methods. We hypothesize that the reciprocating denoising process in diffusion models may inherently enhance the robustness of the watermark when faced with strong attacks and validate the hypothesis.

The case of the Zodiac Killer is one of the most widely known unsolved serial killer cases in history. The unidentified killer murdered five known victims and terrorized the state of California. He also communicated extensively with the press and law enforcement. Besides his murders, Zodiac was known for his use of ciphers. The first Zodiac cipher was solved within a week of its publication, while the second cipher was solved by the authors after 51 years, when it was discovered to be a transposition and homophonic substitution cipher with unusual qualities. In this paper, we detail the historical significance of this cipher and the numerous efforts which culminated in its solution.

Watermarking images is critical for tracking image provenance and claiming ownership. With the advent of generative models, such as stable diffusion, able to create fake but realistic images, watermarking has become particularly important, e.g., to make generated images reliably identifiable. Unfortunately, the very same stable diffusion technology can remove watermarks injected using existing methods. To address this problem, we present a ZoDiac, which uses a pre-trained stable diffusion model to inject a watermark into the trainable latent space, resulting in watermarks that can be reliably detected in the latent vector, even when attacked. We evaluate ZoDiac on three benchmarks, MS-COCO, DiffusionDB, and WikiArt, and find that ZoDiac is robust against state-of-the-art watermark attacks, with a watermark detection rate over 98% and a false positive rate below 6.4%, outperforming state-of-the-art watermarking methods. Our research demonstrates that stable diffusion is a promising approach to robust watermarking, able to withstand even stable-diffusion-based attacks.

More and more people talk about Artificial Intelligence, especially in the last few years. Humans have been brave enough to think about the possibility of robots that can do things that humans do for a long time. Even though this has been helpful in many ways, have we ever wondered if artificial intelligence could be smarter than humans? Before you can compare human and artificial intelligence, you need to know what artificial intelligence is. In simple terms, artificial intelligence is the set of skills that a machine needs to be able to do tasks that a human can do easily.

We're in the Wild West of artificial intelligence development and it is indeed an exciting time. Whether you fear AI or are part of the revolution, the rapid pace of development is showing no signs of slowing down. With advanced AI solutions popping up from every corner of the United States, AI is an equal-opportunist: a national landscape for developers across the country to shine. Tech giants like IBM and Microsoft are constantly iterating artificial intelligence engines to perform tasks from object recognition to transcription, and Watson is a household name-the AI landscape leaders are set, right? The limitation of AI tech coming out of these mega-companies is usability-applying the tech to extract meaningful, actionable data. It works, but does it matter?

Not long ago, I stepped into a lift on the 18th floor of a tall building in New York City. A young woman inside the lift was looking down at the top of her toddler's head with embarrassment as he looked at me and grinned. When I turned to push the ground-floor button, I saw that every button had already been pushed. Kids love pushing buttons, but they only push every button when the buttons light up. From a young age, humans are driven to learn, and learning involves getting as much feedback as possible from the immediate environment. The toddler who shared my elevator was grinning because feedback – in the form of lights or sounds or any change in the state of the world – is pleasurable. In 2012, an ad agency in Belgium produced an outdoor campaign for a TV channel that quickly went viral. The campaign's producers placed a big red button on a pedestal in a quaint square in a sleepy town in Flanders. A big arrow hung above the button with a simple instruction: Push to add drama. You can see the glint in each person's eye as he or she approaches the button – the same glint that came just before the toddler in my elevator raked his tiny hand across the panel of buttons.