Artificial Intelligence (AI) has made it possible for anyone to create images, audio, and video with unprecedented ease, enriching education, communication, and creative expression. At the same time, the rapid rise of AI-generated media has introduced serious risks, including misinformation, identity misuse, and the erosion of public trust as synthetic content becomes increasingly indistinguishable from real media. Although deepfake detection has advanced, many existing tools remain closed-source, limited in modality, or lacking transparency and educational value, making it difficult for users to understand how detection decisions are made. To address these gaps, we introduce SynthGuard, an open, user-friendly platform for detecting and analyzing AI-generated multimedia using both traditional detectors and multimodal large language models (MLLMs). SynthGuard provides explainable inference, unified image and audio support, and an interactive interface designed to make forensic analysis accessible to researchers, educators, and the public. The SynthGuard platform is available at: https://in-engr-nova.it.purdue.edu/

AI-Generated (AIG) content has become increasingly widespread by recent advances in generative models and the easy-to-use tools that have significantly lowered the technical barriers for producing highly realistic audio, images, and videos through simple natural language prompts. In response, platforms are adopting provable provenance with platforms recommending AIG to be self-disclosed and signaled to users. However, these indicators may be often missed, especially when they rely solely on visual cues and make them ineffective to users with different sensory abilities. To address the gap, we conducted semi-structured interviews (N=28) with 15 sighted and 13 BLV participants to examine their interaction with AIG content through self-disclosed AI indicators. Our findings reveal diverse mental models and practices, highlighting different strengths and weaknesses of content-based (e.g., title, description) and menu-aided (e.g., AI labels) indicators. While sighted participants leveraged visual and audio cues, BLV participants primarily relied on audio and existing assistive tools, limiting their ability to identify AIG. Across both groups, they frequently overlooked menu-aided indicators deployed by platforms and rather interacted with content-based indicators such as title and comments. We uncovered usability challenges stemming from inconsistent indicator placement, unclear metadata, and cognitive overload. These issues were especially critical for BLV individuals due to the insufficient accessibility of interface elements. We provide practical recommendations and design implications for future AIG indicators across several dimensions.

The images and videos were hard to miss in the days leading up to November 5. There was Donald Trump with the chiseled musculature of Superman, hovering over a row of skyscrapers. People had clearly used AI to create these--an effort to show support for their candidate or to troll their opponents. But the images didn't stop after Trump won. The day after polls closed, the Statue of Liberty wept into her hands as a drizzle fell around her. Trump and Elon Musk, in space suits, stood on the surface of Mars; hours later, Trump appeared at the door of the White House, waving goodbye to Harris as she walked away, clutching a cardboard box filled with flags.

AI-generated media has become a threat to our digital society as we know it. These forgeries can be created automatically and on a large scale based on publicly available technology. Recognizing this challenge, academics and practitioners have proposed a multitude of automatic detection strategies to detect such artificial media. However, in contrast to these technical advances, the human perception of generated media has not been thoroughly studied yet. In this paper, we aim at closing this research gap. We perform the first comprehensive survey into people's ability to detect generated media, spanning three countries (USA, Germany, and China) with 3,002 participants across audio, image, and text media. Our results indicate that state-of-the-art forgeries are almost indistinguishable from "real" media, with the majority of participants simply guessing when asked to rate them as human- or machine-generated. In addition, AI-generated media receive is voted more human like across all media types and all countries. To further understand which factors influence people's ability to detect generated media, we include personal variables, chosen based on a literature review in the domains of deepfake and fake news research. In a regression analysis, we found that generalized trust, cognitive reflection, and self-reported familiarity with deepfakes significantly influence participant's decision across all media categories.

There's a big protest going on at Artstation against AI generated images. Personally, I think we, as a society, aren't ready for what AI-generated media will bring. As a writer, I'm terrified that someone soon will be able to say "write me a sci-fi novel about black holes" and the AI will spit out a 120,000-word book that some publisher might actually print and the average reader might consider "good." I labor over each word, sentence, chapter, and overarching story, and most novels take me over a year to write. And now someone will soon recreate this with a few mouse clicks.

China's Cyberspace Administration recently issued regulations prohibiting the creation of AI-generated media without clear labels, such as watermarks--among other policies--reports The Register. The new rules come as part of China's evolving response to the generative AI trend that has swept the tech world in 2022, and they will take effect on January 10, 2023. In China, the Cyberspace Administration oversees the regulation, oversight, and censorship of the Internet. Under the new regulations, the administration will keep a closer eye on what it calls "deep synthesis" technology. In a news post on the website of China's Office of the Central Cyberspace Affairs Commission, the government outlined its reasons for issuing the regulation.

Ben Meisner is the founder of the leading online photo editing platform Ribbet.com. In recent times a flurry of AI-powered photo and video editing tools have emerged, ranging from those that simply save time by automatically removing backgrounds, to more sophisticated tools that replace people (deepfakes) or that generate completely new and realistic scenes from scratch, such as DALL·E and Stable Diffusion. When you consider the development of AI technology in driverless cars, there are clear problems that are being addressed. For example, there's the potential benefit of fewer accidents, where approximately 1.3 million people currently lose their lives each year in road fatalities. But when it comes to photo and video manipulation, what is it we are ultimately working toward solving?

What's this about: AI-generated media is one tool in the AI arsenal being deployed by governments and independent actors all across the globe. This new and effective tool is often used to target political parties and figures. At the same time, AI-generated media can help political parties become more in touch with their base, and it's a key tool in the fight against disinformation. With a political environment as strained as ours, one single piece of fake media can throw our world upside down, which is why it's crucial for us to understand its involvement in today's politics. Deepfakes, which are usually a type of fake media involving images and videos, have been making a lot of headlines over the last few years, and not for good reasons.

What's this about: AI-generated media is one tool in the AI arsenal being deployed by governments and independent actors all across the globe. This new and effective tool is often used to target political parties and figures. At the same time, AI-generated media can help political parties become more in touch with their base, and it's a key tool in the fight against disinformation.