Position: AI/ML Deepfake Research is Misaligned with AI-Generated Non-Consensual Intimate Imagery (AIG-NCII)

AI 中文解读

核心亮点：一项新研究指出，AI领域对“深度伪造”的热点关注跑偏了——大家都在忙着辨别真假，却忽视了最该解决的“非自愿色情图片”滥用问题。通俗解读：深度伪造技术经常被用来制作色情内容，受害者往往是被迫卷入的普通人。可现有的AI研究几乎全在琢磨怎么判断一张图是不是伪造的，比如防诈骗、防假新闻。但这其实没抓到痛点：哪怕你知道某张照片是AI合成的，它对受害者的伤害并不会减少，甚至可能因为被反复讨论而雪上加霜。打个比方，就像有人用你的脸P了一张恶心图片发到网上，大家说“这图是假的”，但你被羞辱、被网暴的痛苦一点都没减轻。研究团队认为，当前的技术手段只关心“看的人”会不会上当，却忽略了“被拍的人”尊严受到的直接伤害。实际影响：这意味着如果你或身边的人不幸成为这类图片的受害者，现有的技术工具很难提供实际帮助——它们最多告诉你“这是假图”，却无法阻止图片传播、删除内容或追究责任。未来，研究者呼吁AI安全领域应该重新调整方向，把保护受害者尊严放在首位，比如开发能快速识别、标记并阻断这类图片传播的工具，同时联合反性暴力专家共同制定安全准则。对普通人来说，这或许能推动更有人情味的科技治理：技术不再只盯着“真假”，而是真正为被伤害的人撑腰。

arXiv:2607.18263v1 Announce Type: new Abstract: 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.