一篇新研究论文提出了一种通过评估真实图像抵抗生成模型重建的能力来认证其真实性的方法。研究发现，现有的深度伪造检测器准确率随时间推移而下降，对抗性攻击将其有效性降低到2%以下。提出的“校准再合成”方法旨在认证一幅图像为真实，前提是没有任何已知生成器能够忠实地重建它，将生成图像的错误认证率限制在1%。然而，随着生成器的改进，仅凭内容验证真实性的能力正在减弱，使得区分真实媒体和合成媒体变得更加困难。 AI
影响 区分真实图像和AI生成图像的难度日益增加，对在线内容验证和信任构成了挑战。
排序理由 研究论文发布在arXiv和Hugging Face上。
在 Hugging Face Daily Papers 阅读 →
AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →
完整方法见我们的编辑标准。
Generative models can synthesize high-quality inauthentic multimedia content that is already being misused at scale. We evaluate twenty deepfake detectors against ten generators released in the last four years and find accuracy decreasing over time, from near-perfect 99.5% to 76%…
Generative models can synthesize high-quality inauthentic multimedia content that is already being misused at scale. We evaluate twenty deepfake detectors against ten generators released in the last four years and find accuracy decreasing over time, from near-perfect 99.5% to 76%…