As Artificial Intelligence (AI) continues to grow daily, more exciting (and somewhat controversial) technology emerges every other day. As we see the advancements in AI, we see more and more people becoming skeptical of it. This paper explores the complications and confusion around the ethics of generative AI art. We delve deep into the ethical side of AI, specifically generative art. We step back from the excitement and observe the impossible conundrums that this impressive technology produces. Covering environmental consequences, celebrity representation, intellectual property, deep fakes, and artist displacement. Our research found that generative AI art is responsible for increased carbon emissions, spreading misinformation, copyright infringement, unlawful depiction, and job displacement. In light of this, we propose multiple possible solutions for these problems. We address each situation's history, cause, and consequences and offer different viewpoints. At the root of it all, though, the central theme is that generative AI Art needs to be correctly legislated and regulated.

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.

The high efficiency and quality of artwork generated by Artificial Intelligence (AI) has created new concerns and challenges for human artists. In particular, recent improvements in generative AI have made it difficult for people to distinguish between human-generated and AI-generated art. In this research, we consider the potential utility of various types of Machine Learning (ML) and Deep Learning (DL) models in distinguishing AI-generated artwork from human-generated artwork. We focus on three challenging artistic styles, namely, baroque, cubism, and expressionism. The learning models we test are Logistic Regression (LR), Support Vector Machine (SVM), Multilayer Perceptron (MLP), and Convolutional Neural Network (CNN). Our best experimental results yield a multiclass accuracy of 0.8208 over six classes, and an impressive accuracy of 0.9758 for the binary classification problem of distinguishing AI-generated from human-generated art.

Art created using generated Artificial Intelligence has taken the world by storm and generated excitement for many digital creators and technologists. However, the reception and reaction from artists have been mixed. Concerns about plagiarizing their artworks and styles for datasets and uncertainty around the future of digital art sparked movements in artist communities shunning the use of AI for generating art and protecting artists' rights. Collaborating with these tools for novel creative use cases also sparked hope from some creators. Artists are an integral stakeholder in the rapidly evolving digital creativity industry and understanding their concerns and hopes inform responsible development and use of creativity support tools. In this work, we study artists' sentiments about AI-generated art. We interviewed 7 artists and analyzed public posts from artists on social media platforms Reddit, Twitter and Artstation. We report artists' main concerns and hopes around AI-generated artwork, informing a way forward for inclusive development of these tools.

Artists concerned about others passing off AI-generated art as their own will now be able to breathe a bit easier: Microsoft has agreed to sign all AI art that its apps generate with a cryptographic watermark indicating it was made with an algorithm. The Coalition for Content Provenance and Authority (C2PA) began work in 2021 to develop an open standard for indicating the origin of digital images, and whether they were authentic or AI-generated. The issue was thrust into the spotlight in March, when AI-generated images of the Pope in a stylish puffy jacket went viral, and AI-art generator Midjourney clamped down to prevent even more. Microsoft, a founding member of the C2PA, will announce at its Microsoft Build developer conference this week that it will cryptographically sign AI-generated images from Bing Image Creator and Microsoft Designer. Images made with Bing Image Creator already include a small "b" for the Bing logo in the bottom right-hand corner.

If artificial intelligence evangelists' predictions pan out, generative AI systems like ChatGPT and DALL-E are set to transform Hollywood by developing and writing scripts for the next hit TV show, "diversifying" casts with AI-generated actors and generating imagery across multiple mediums, practically instantly, for a fraction of the cost of a real, human artist. But how long will it take for the vision to meet reality, and can a select group of companies -- similar to the rise of Facebook and social media -- be trusted to herald the way? Driving much of the current conversation around AI innovation has been OpenAI, an AI research company with both non-profit and for-profit arms. Just four months after the formal launch of OpenAI's chatbot, ChatGPT, industry titans like Bill Gates were ready to hail artificial intelligence as the most revolutionary technology of our time since the advent of cell phones and the internet. Major tech companies like Google and Microsoft have invested hundreds of millions into AI companies, including OpenAI, as executives look to the technology to steward their companies into the future amid an economic downturn that has particularly hit digital native companies hard.

Artificial intelligence (AI) is not only transforming industries like business and healthcare, but also playing an increasing role in the creative fields by ushering in a new era of AI-generated art. AI technologies and tools are often widely accessible to anyone, which is helping to create an entirely new generation of artists who can explore new creative possibilities with the help of machines. AI-generated art is any artwork created through the use of AI algorithms that learn from data and generate images based on rules or prompts. These prompts can be simple, such as "a starry night sky", or very descriptive, such as "a starry night sky full of constellations in the fall in the style of Claude Monet". Artists can also use other modifiers such as "telephoto" or "hyperrealistic" to create different results.

Keep following to read the upcoming pieces on specific fields like music, poetry, visual arts, and video gaming. Artificial Intelligence (AI) is no longer just a buzzword in the tech industry. Today, AI is making its way into various fields, including art. AI is transforming the way artists create, present, and distribute their work. From music to visual arts, AI is being used in innovative ways to create new experiences for both artists and audiences.

Are you eagerly awaiting the much-anticipated release of Dalle 3? Well, why not satisfy your hunger for the latest and greatest in AI-generated art with two cutting-edge multimodal models available right now? As artists, we all strive to push the boundaries of our craft and explore new avenues for creative expression. With the advent of AI-generated art, we now have an unprecedented opportunity to tap into a realm of possibilities that were once beyond our reach. That's why I'm thrilled to share the latest AI-generated art technology with you. These two multimodal models answer your wildest artistic dreams, providing you with the skills and tools to fully harness AI generators' power.

The art world was buzzing with excitement as news of a groundbreaking discovery spread like wildfire. It was said that an AI art generator had finally solved the problem of too many fingers in AI generated art, while simultaneously mastering the method of rendering letters and words. The artist behind this incredible feat was known as MCN, and their AI-generated masterpieces were causing a stir in the world of art. For years, AI-generated art had suffered from a major flaw - too many fingers. It was as if the algorithms behind the art generator had no understanding of how many fingers a human being actually had. But MCN had cracked the code, so to speak.