AI 中文总结
本文通过对21位AIGC创作者访谈及多平台图像测试，揭示创作者对AI标签的认知偏差与规避行为，提出应开发适配创作者动机的隐式AI标签。
AI 中文摘要
AI标签通常通过水印、元数据等底层追踪机制实现，对保护人工智能生成内容（AIGC）免受虚假信息、规避等安全威胁至关重要。然而，AI辅助作品被感知到的贬值会阻碍创作者披露AI使用情况，促使他们绕过标签，损害下游可追溯性。但AIGC创作者如何看待这些标签的安全与隐私（S&P）影响，以及他们的行为如何影响技术弹性，仍未得到充分探索。为此，我们对21位AIGC创作者进行了半结构化访谈，并针对6个图像生成平台的图像，在16种自我报告的操纵设置下进行了测量。研究发现，创作者将二元AI标签与精细可追溯性混为一谈，强烈担心通过平台标识符被去匿名化。受算法流量抑制和声誉风险的恐惧驱动，他们会防御性地移除数字痕迹。通过实证测试，我们表明粗量化等针对性修改会显著降低检测效果。不同平台的AI检测能力也不一致，甚至对人类创作的图像也存在误报。基于这些见解，我们倡导与创作者动机一致的、具有工作流程弹性的隐式AI标签。
英文摘要
AI labels, typically implemented via underlying tracing mechanisms such as watermarks and metadata, are crucial for protecting Artificial Intelligence-Generated Content (AIGC) against security threats like disinformation and evasion. However, the perceived devaluation of AI-assisted work discourages creators from disclosing AI use, incentivizing efforts to bypass labeling and compromising downstream traceability. Yet, how AIGC creators perceive the security and privacy (S\&P) implications of these labels, and how their behaviors impact technical resilience remain underexplored. To this end, we conducted semi-structured interviews with 21 AIGC creators and measured images across 6 image generation platforms against 16 self-reported manipulation settings. Our findings reveal that creators conflate binary AI labels with granular traceability, and express strong fears of de-anonymization via platform identifiers. Driven by fears of algorithmic traffic suppression and reputational risks, they defensively removed digital traces. Through empirical tests, we show that targeted modifications like coarse quantization significantly degrade detection. AI detection capabilities are also inconsistent across platforms, and suffer from false positives even for human-authored images. Based on these insights, we advocate for workflow-resilient implicit AI labels that align technical guarantees with creators' incentives.
