TL;DR
This entry introduces a code-book with a set of four key concepts and related variables and operational definitions designed to allow coders to consistently identify potentially problematic characteristics of AI-generated in-formational images.
Synthetic media are digital artifacts (image, video, audio, text, or multimodal content) that are wholly or partly generated or materially manipulated by artificial intelligence (AI), particularly by deep-learning models. In consequence, their form, source, identity signal, or evidentiary relation to recorded events be...
Generative image models can produce highly realistic images, raising concerns about potential misuse in creating deceptive content. Current deepfake approaches face several challenges, including limited generalizability, lack of interpretability, and poor actionability. To help address these, we present ASAP, an intera...
Results indicate that using narrowly focused datasets specific to each brand is more effective than combining data from multiple brands with mixed personalities, and suggest that VLMs have the potential to support content creators by reducing manual effort, enhancing linguistic nuance, and maintaining brand identity in...
Text-to-image generative models can produce highly realistic imagery but also raise concerns about harmful misuse. While safety mechanisms exist, systematic evaluations of their effectiveness against realistic attacks remain limited. We present a systematic evaluation of harmful content generation across five open text...
It is shown how perceived authenticity is formed, revised and sometimes unsettled during encounters with AIGIs, and HSM is extended into a heritage context where authenticity is tied to cultural memory, institutional trust and prior experience.
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