Synthetic Photography Detection: A Visual Guidance for Identifying Synthetic Images Created by AI
This work addresses the threat of malicious use of synthetic images for deception, which is a problem for security analysts and the general public, but it appears incremental as it builds on known flaws in existing models.
The paper tackles the problem of identifying AI-generated synthetic images that resemble real photographs, by examining visible artifacts in images from recent generative diffusion models and categorizing them to aid detection.
Artificial Intelligence (AI) tools have become incredibly powerful in generating synthetic images. Of particular concern are generated images that resemble photographs as they aspire to represent real world events. Synthetic photographs may be used maliciously by a broad range of threat actors, from scammers to nation-state actors, to deceive, defraud, and mislead people. Mitigating this threat usually involves answering a basic analytic question: Is the photograph real or synthetic? To address this, we have examined the capabilities of recent generative diffusion models and have focused on their flaws: visible artifacts in generated images which reveal their synthetic origin to the trained eye. We categorize these artifacts, provide examples, discuss the challenges in detecting them, suggest practical applications of our work, and outline future research directions.