构建大规模伪真图像数据集，评估AI生成图像检测器的鲁棒性。
Semi-Truths: A Large-Scale Dataset of AI-Augmented Images for Evaluating Robustness of AI-Generated Image detectors
- 基于多种扩散模型和增强技术生成局部扰动图像
- 涵盖147万张增广图像，验证检测器对不同扰动的敏感度
- 适合研究检测器可靠性与公平性的研究人员
文本到图像扩散模型在艺术、设计和娱乐领域有广泛应用，但也可能被用于制造和传播虚假信息。尽管近期出现了声称具备鲁棒性的AI生成图像检测器，但其真实有效性仍不明确。为探究这一问题，我们提出SEMI-TRUTHS数据集，包含27,600张真实图像、223,400张掩码和1,472,700张经局部扰动处理的AI增广图像，采用多种增强技术、扩散模型和数据分布生成。每张增广图像均配有元数据，支持标准化的检测器鲁棒性评估。实验表明，现有先进检测器对不同类型的扰动、扰动程度、数据分布及增强方法表现出差异化的敏感性，揭示了其性能边界。增广与评估流程代码已开源于https://github.com/J-Kruk/SemiTruths。
原文摘要 · Abstract (English)
Text-to-image diffusion models have impactful applications in art, design, and entertainment, yet these technologies also pose significant risks by enabling the creation and dissemination of misinformation. Although recent advancements have produced AI-generated image detectors that claim robustness against various augmentations, their true effectiveness remains uncertain. Do these detectors reliably identify images with different levels of augmentation? Are they biased toward specific scenes or data distributions? To investigate, we introduce SEMI-TRUTHS, featuring 27,600 real images, 223,400 masks, and 1,472,700 AI-augmented images that feature targeted and localized perturbations produced using diverse augmentation techniques, diffusion models, and data distributions. Each augmented image is accompanied by metadata for standardized and targeted evaluation of detector robustness. Our findings suggest that state-of-the-art detectors exhibit varying sensitivities to the types and degrees of perturbations, data distributions, and augmentation methods used, offering new insights into their performance and limitations. The code for the augmentation and evaluation pipeline is available at https://github.com/J-Kruk/SemiTruths.
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