DFA-CON learns a discriminative representation space, posing affinity among original artworks and their forged counterparts within a contrastive learning framework. The model is trained across multiple attack types, including inpainting, style transfer, adversarial perturbation, and cutmix. Evaluation results demonstrate robust detection performance across most attack types, outperforming recent pretrained foundation models. Code and model checkpoints will be released publicly upon acceptance.

Text-to-image models based on diffusion processes, such as DALL-E, Stable Diffusion, and Midjourney, are capable of transforming texts into detailed images and have widespread applications in art and design. As such, amateur users can easily imitate professional-level paintings by collecting an artist's work and fine-tuning the model, leading to concerns about artworks' copyright infringement. To tackle these issues, previous studies either add visually imperceptible perturbation to the artwork to change its underlying styles (perturbation-based methods) or embed post-training detectable watermarks in the artwork (watermark-based methods). However, when the artwork or the model has been published online, i.e., modification to the original artwork or model retraining is not feasible, these strategies might not be viable. To this end, we propose a novel method for data-use auditing in the text-to-image generation model. The general idea of ArtistAuditor is to identify if a suspicious model has been finetuned using the artworks of specific artists by analyzing the features related to the style. Concretely, ArtistAuditor employs a style extractor to obtain the multi-granularity style representations and treats artworks as samplings of an artist's style. Then, ArtistAuditor queries a trained discriminator to gain the auditing decisions. The experimental results on six combinations of models and datasets show that ArtistAuditor can achieve high AUC values (> 0.937). By studying ArtistAuditor's transferability and core modules, we provide valuable insights into the practical implementation. Finally, we demonstrate the effectiveness of ArtistAuditor in real-world cases by an online platform Scenario. ArtistAuditor is open-sourced at https://github.com/Jozenn/ArtistAuditor.

It is evident that, currently, generative models are surpassed in quality by human professionals. However, with the advancements in Artificial Intelligence, this gap will narrow, leading to scenarios where individuals who have dedicated years of their lives to mastering a skill become obsolete due to their high costs, which are inherently linked to the time they require to complete a task -- a task that AI could accomplish in minutes or seconds. To avoid future social upheavals, we must, even now, contemplate how to fairly assess the contributions of such individuals in training generative models and how to compensate them for the reduction or complete loss of their incomes. In this work, we propose a method to structure collaboration between model developers and data providers. To achieve this, we employ Shapley Values to quantify the contribution of artist(s) in an image generated by the Stable Diffusion-v1.5 model and to equitably allocate the reward among them.

Art curatorial practice is characterized by the presentation of an art collection in a knowledgeable way. Machine processes are characterized by their capacity to manage and analyze large amounts of data. This paper envisages AI curation and audience interaction to explore the implications of contemporary machine learning models for the curatorial world. This project was developed for the occasion of the 2023 Helsinki Art Biennial, entitled New Directions May Emerge. We use the Helsinki Art Museum (HAM) collection to re-imagine the city of Helsinki through the lens of machine perception. We use visual-textual models to place indoor artworks in public spaces, assigning fictional coordinates based on similarity scores. We transform the space that each artwork inhabits in the city by generating synthetic 360 art panoramas. We guide the generation estimating depth values from 360 panoramas at each artwork location, and machine-generated prompts of the artworks. The result of this project is an AI curation that places the artworks in their imagined physical space, blurring the lines of artwork, context, and machine perception. The work is virtually presented as a web-based installation on this link http://newlyformedcity.net/, where users can navigate an alternative version of the city while exploring and interacting with its cultural heritage at scale.

I've been fascinated by new technologies my entire life. First entranced by computers in 1980 when I purchased a TRS-80 model I with 4K of RAM, I am always watching for the next big thing. All those years ago, I recall playing with a program called "Eliza". Designed to be an interactive computer therapist, this was my first encounter with artificial intelligence. I thought it was pretty cool.

As someone working in a creative field, I've never been concerned about a computer taking my job. I always felt confident that the tasks required of me as a photo editor for New York Magazine are too complex and messy -- too human -- for an artificial intelligence to perform. That is, until DALL-E 2, a sophisticated AI that generates original artwork based only on text input, opened to public beta last June. It's easy to lose hours on the r/dalle2 subreddit, where beta testers have been posting their work. More often than not, the only way to differentiate a DALL-E creation from a human-generated image is five colorful squares tucked in the bottom right corner of each composition -- DALL-E's signature.

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! Before writing a single word of this article, I created the image above using a new type of AI software that produces "generative artwork." The process took about 15 minutes and did not involve paints or canvases. I simply entered a few lines of text to describe the image that I wanted – a robot holding a paintbrush and standing at an easel.

Valhil NFT I, LLC, a wholly owned subsidiary of Valhil Capital, LLC, issued 9 NFTs of 10 minted NFTs in a series titled "Buen Viaje" as securities for the first time in history, through a competitive sale that occurred on October 8, 2021 at the Texas Blockchain Summit. The Offering was underwritten by Valhil Capital, LLC. The NFT securities were marketed and sold to "accredited investors" in a private transaction in reliance on, and in compliance with, an exemption from the registration requirements of the Securities Act provided by Rule 506(c) of Regulation D under the Securities Act. The NFT securities are "restricted securities" as defined in Rule 144 under the Securities Act. The original canvas painting was created live by Mr. Rolando Diaz.