构建合成灾害检测基准，揭示现有方法在跨域场景下的严重失效。
Forged Calamity: Benchmark for Cross-Domain Synthetic Disaster Detection in the Age of Diffusion
- 构建3万张图像的合成灾害数据集，涵盖4种扩散模型生成样本。
- 现有检测器在未见生成器或灾害类型下准确率最高下降50%。
- 强调需发展不依赖特定模型和领域的通用检测方法。
文本到图像扩散模型的快速发展，使得高度逼真的合成图像与真实照片难以区分，给网络安全、数字取证及灾情应对带来挑战。虚假的洪水、火灾或地震图像可能传播错误信息或干扰应急响应。为此，我们提出Forged Calamity基准数据集，包含30,000张图像（6,000张真实图像与24,000张由四种扩散模型生成的合成图像）。在微调与零样本设置下的综合实验表明，当前取证方法存在持续性的泛化缺陷：微调检测器在分布内表现良好，但在未见生成器或灾害类型下准确率最高下降50%，显示出对模型特异性伪影的过拟合；零样本通用检测器也难以保持稳定性能，仅有少数具备代表鲁棒性的模型表现出有限抗性。这些发现凸显了持久的泛化差距，迫切需要面向领域和模型无关的检测方法以保障扩散时代的视觉真实性。
原文摘要 · Abstract (English)
The rapid advancement of text-to-image diffusion models has enabled the creation of highly photorealistic synthetic images that closely resemble real photographs, making it increasingly difficult to distinguish authentic content from AI-generated fabrications. This poses challenges for cybersecurity, digital forensics, and disaster response, where fake imagery of floods, fires, or earthquakes can spread misinformation or disrupt emergency operations. To address this, we introduce Forged Calamity, a benchmark dataset for synthetic disaster detection containing 30,000 images, including 6,000 real and 24,000 synthetic samples generated by four diffusion models. Comprehensive experiments across fine-tuned and zero-shot settings reveal consistent weaknesses in current forensic approaches. Fine-tuned detectors perform well in-distribution but lose up to 50\% accuracy on unseen generators or disaster types, showing overfitting to model-specific artifacts. Zero-shot generalized detectors also struggle to maintain stable accuracy, with only limited resilience in a few representation-robust models. These findings highlight persistent generalization gaps and the urgent need for domain- and model-agnostic detection methods to ensure visual authenticity in the diffusion era.
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。