用部件级结构化推理拆穿AI伪造，让深度伪造检测不再只是黑箱判断
arXiv:2603.21526v2 Announce Type: replace Abstract: Multimodal large language models (MLLMs) offer a promising path toward interpretable deepfake dete…
免训练多模态大模型结合可解释AI，为语音深度伪造检测自动生成可信解释。
arXiv:2606.16137v1 Announce Type: cross Abstract: Speech deepfake detection (SDD) systems require trustworthy explanations for reliable decision-makin…
深度伪造检测新突破：兼顾校准、公平与准确性，为AI安全提供可靠新方法。
arXiv:2606.09881v1 Announce Type: new Abstract: Deepfake detectors show large performance gaps across demographic groups. Existing fairness approaches…
利用语言特征增强音频数据，为深度伪造语音检测开辟新思路
arXiv:2606.10246v1 Announce Type: cross Abstract: Maliciously-created fake speech, including deepfaked and spoofed audio, is proliferating at an alarm…
用转向向量（Steering Vectors）精准识别AI生成文本，为深度伪造检测提供新思路。
arXiv:2606.07313v1 Announce Type: cross Abstract: Detecting machine-generated text is especially difficult under distribution shift, such as transfer …
医学深度伪造检测新方法，通过伪造感知推理实现高可解释性与鲁棒性，为医疗影像安全提供创新思路。
arXiv:2603.18577v2 Announce Type: replace Abstract: Text-guided image editors can now manipulate authentic medical scans with high fidelity, enabling …
语言模型训练如何提升深度伪造检测器的泛化能力，揭秘正则化的关键作用。
arXiv:2605.31192v1 Announce Type: new Abstract: Recently, thanks to the advent of Multimodal-LLMs, deepfake detectors are striving not only to be gene…
揭秘视觉基础模型在面部深度伪造检测中的跨域泛化极限，为AI安全提供关键洞察。
arXiv:2605.24965v1 Announce Type: cross Abstract: The rapid evolution of generative models has enabled the creation of hyper-realistic facial deepfake…