仅通过API接口检测生成图像，无需模型权重或大量真实数据。
Where's the liability in the Generative Era? Recovery-based Black-Box Detection of AI-Generated Content
- 用掩码重建能力判断图像是否为生成内容
- 在8个扩散模型上平均精度提升4.31%
- 适合无模型访问权限的场景，如内容审核
生成模型产生的逼真图像日益难以与真实图像区分，引发对虚假信息和欺诈的担忧。现有检测方法多依赖模型权重或大规模真实图像数据集，限制了实际应用。本文提出一种仅需API访问的黑盒检测框架，通过掩码部分图像并评估模型重建能力，判断其生成可能性。对于不支持掩码输入的模型，引入低成本代理模型以匹配目标模型分布，增强检测效果。实验表明，该方法在八个扩散模型变体数据集上平均精度提升4.31%，显著优于基线。
原文摘要 · Abstract (English)
The recent proliferation of photorealistic images created by generative models has sparked both excitement and concern, as these images are increasingly indistinguishable from real ones to the human eye. While offering new creative and commercial possibilities, the potential for misuse, such as in misinformation and fraud, highlights the need for effective detection methods. Current detection approaches often rely on access to model weights or require extensive collections of real image datasets, limiting their scalability and practical application in real world scenarios. In this work, we introduce a novel black box detection framework that requires only API access, sidestepping the need for model weights or large auxiliary datasets. Our approach leverages a corrupt and recover strategy: by masking part of an image and assessing the model ability to reconstruct it, we measure the likelihood that the image was generated by the model itself. For black-box models that do not support masked image inputs, we incorporate a cost efficient surrogate model trained to align with the target model distribution, enhancing detection capability. Our framework demonstrates strong performance, outperforming baseline methods by 4.31% in mean average precision across eight diffusion model variant datasets.
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