AI 中文总结
研究指出人工智能/机器学习领域对深度伪造的研究与AIG-NCII不一致，现有技术干预忽略AIG-NCII，现有干预只关注观众认知危害，忽略主体尊严危害，建议更新威胁模型，在安全研究中解决AIG-NCII，同时警告研究人员涉足该领域需谨慎并做好防护。
AI 中文摘要
人工智能生成的非自愿亲密图像（AIG-NCII）在人工智能/机器学习文献中关于人工智能生成媒体（通常称为“深度伪造”）的讨论中未得到充分解决。当前对深度伪造的研究集中在认知危害，与涉及性化图像的生成式人工智能滥用的主导现实不一致。通过对高引用作品的全景分析表明，处理深度伪造的技术干预几乎完全忽略了AIG-NCII，将研究生态系统限制在真实性检测工具上。现有干预措施解决以观众为中心的认知危害，却忽略了以主体为中心的尊严危害。知道图像是合成的并不能减轻对主体的伤害，在某些情况下甚至可能加剧。最后提出重新调整该领域的建议，包括更新威胁模型以考虑以主体为中心的危害，并在人工智能安全研究中解决AIG-NCII。还警告研究人员只有实施安全防护措施并与性暴力预防领域专家建立合作关系才能涉足此高风险领域。
英文摘要
AI-generated non-consensual intimate imagery (AIG-NCII) is not adequately addressed in AI/ML literature regarding AI-generated media, commonly referred to as "deepfakes". While research on deepfakes currently focuses on its epistemic harms -- or harms relating to truth and authenticity -- this is misaligned with the dominant reality of generative AI abuse involving sexualized imagery. We conduct a landscape analysis of highly-cited works to demonstrate that technical interventions addressing deepfakes almost entirely ignore AIG-NCII, limiting the research ecosystem to authenticity detection tools. In this position paper, we argue that existing interventions address viewer-centric epistemic harms, such as fraud or scams, but ignore subject-centric dignity harms, such as AIG-NCII. We illustrate that knowing an image is synthetic does not mitigate harms to subjects and may, in some cases, even exacerbate them. We conclude by offering recommendations to realign the field, including updating threat models to consider subject-centric harms and addressing AIG-NCII in AI safety research. Finally, we caution that researchers should only engage in this high-risk domain if they implement safety guardrails for both subjects and researchers and establish partnerships with domain experts in sexual violence prevention.
CommentsICML 2026. Outstanding position paper honorable mention
