DiffusionFF：基于扩散模型的联合人脸伪造检测与细粒度伪影定位框架
DiffusionFF: A Diffusion-based Framework for Joint Face Forgery Detection and Fine-Grained Artifact Localization
将预训练伪造检测器作为编码器、扩散模型作为解码器，利用多尺度伪造特征条件生成细粒度伪影定位图，再与高层语义特征融合提升检测性能；该思路可迁移到其他需要细粒度异常定位的视觉任务。
原文依据 a pretrained forgery detector serves as a powerful "artifact encoder", and a denoising diffusion model is repurposed as an "artifact decoder"
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论深度伪造技术的快速发展要求鲁棒且可靠的人脸伪造检测算法。尽管判断图像是否被操纵仍然至关重要，但精确地定位伪造线索的能力对于增强模型可解释性和建立用户信任同样重要。为应对这一双重挑战，我们提出了DiffusionFF，一个基于扩散模型的框架，可同时执行人脸伪造检测和细粒度伪影定位。我们的核心思想是建立一种新颖的编码器-解码器架构：预训练的伪造检测器作为强大的“伪影编码器”，而去噪扩散模型被重新用作“伪影解码器”。以编码器提取的多尺度伪造相关特征为条件，解码器逐步合成详细的伪影定位图。然后，我们将该细粒度定位图与来自伪造检测器的高层语义特征融合，从而显著提升检测能力。大量实验表明，DiffusionFF在多个基准上达到了最优性能，凸显了其卓越的有效性和可解释性。
英文原文摘要 1197 字符
The rapid evolution of deepfake technologies demands robust and reliable face forgery detection algorithms. While determining whether an image has been manipulated remains essential, the ability to precisely localize forgery clues is also important for enhancing model explainability and building user trust. To address this dual challenge, we introduce DiffusionFF, a diffusion-based framework that simultaneously performs face forgery detection and fine-grained artifact localization. Our key idea is to establish a novel encoder-decoder architecture: a pretrained forgery detector serves as a powerful "artifact encoder", and a denoising diffusion model is repurposed as an "artifact decoder". Conditioned on multi-scale forgery-related features extracted by the encoder, the decoder progressively synthesizes a detailed artifact localization map. We then fuse this fine-grained localization map with high-level semantic features from the forgery detector, leading to substantial improvements in detection capability. Extensive experiments show that DiffusionFF achieves state-of-the-art (SOTA) performance across multiple benchmarks, underscoring its superior effectiveness and explainability.