FerretNet: Efficient Synthetic Image Detection via Local Pixel Dependencies
Shuqiao Liang, Jian Liu, Chen Renzhang, Quanlong Guan
The increasing realism of synthetic images generated by advanced models such as VAEs, GANs, and LDMs poses significant challenges for synthetic image detection.To address this issue, we explore two artifact types introduced during the generation process: (1) latent distribution deviations and (2) decoding-induced smoothing effects, which manifest as inconsistencies in local textures, edges, and color transitions.Leveraging local pixel dependencies (LPD) properties rooted in Markov Random Fields, we reconstruct synthetic images using neighboring pixel information to expose disruptions in texture continuity and edge coherence.Building upon LPD, we propose FerretNet, a lightweight neural network with only 1.1M parameters that delivers efficient and robust synthetic image detection.Extensive experiments demonstrate that FerretNet—trained exclusively on the 4-class ProGAN dataset—achieves an average accuracy of 97.1% on an open-world benchmark comprising over 20 generative models, surpassing state-of-the-art methods by 10.6%. All code and datasets will be publicly released.