AI-generated synthetic media are increasingly used in real-world scenarios, often with the purpose of spreading misinformation and propaganda through social media platforms, where compression and other processing can degrade fake detection cues. Currently, many forensic tools fail to account for these in-the-wild challenges. In this work, we introduce TrueFake, a large-scale benchmarking dataset of 600,000 images including top notch generative techniques and sharing via three different social networks. This dataset allows for rigorous evaluation of state-of-the-art fake image detectors under very realistic and challenging conditions. Through extensive experimentation, we analyze how social media sharing impacts detection performance, and identify current most effective detection and training strategies. Our findings highlight the need for evaluating forensic models in conditions that mirror real-world use.

本研究针对AI生成的合成媒体在社交媒体上传播虚假信息的问题，提出了TrueFake数据集，包含60万幅图像，并在三种不同社交网络上共享。实验表明，社交媒体的分享显著影响了假图像检测的性能，强调了在真实使用环境中评估取证模型的重要性。

TrueFake：最新一代假图像在社交网络上的真实案例数据集