通过局部像素依赖关系，高效识别生成图像的细微纹理异常。
FerretNet: Efficient Synthetic Image Detection via Local Pixel Dependencies
- 利用邻近像素关联性重建图像，暴露生成过程中的纹理断裂
- 仅用110万参数，在22个模型上平均准确率达97.1%
- 轻量设计适合部署，适用于真实世界检测场景
先进的生成模型（如VAEs、GANs和LDMs）生成的合成图像越来越逼真，给检测带来挑战。本文分析生成过程中引入的两类伪影：(1) 潜在分布偏差，(2) 解码引起的平滑效应，表现为局部纹理、边缘和色彩过渡不一致。基于马尔可夫随机场的局部像素依赖（LPD）特性，我们利用邻近像素信息重构图像，揭示纹理连续性和边缘一致性被破坏的迹象。在此基础上，提出FerretNet——一个仅含110万参数的轻量级神经网络，实现高效且鲁棒的合成图像检测。大量实验表明，仅在4类ProGAN数据集上训练的FerretNet，即可在包含22种生成模型的开放世界基准上达到平均97.1%的准确率。代码与数据集已公开于https://github.com/xigua7105/FerretNet。
原文摘要 · Abstract (English)
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 22 generative models. Our code and datasets are publicly available at https://github.com/xigua7105/FerretNet.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。