用跨尺度Transformer提升深度伪造检测准确率
Cross multiscale vision transformer for deep fake detection
- 设计跨尺度视觉Transformer捕捉多层级图像特征
- 在SP Cup 2025数据集上达到98.7%检测准确率
- 适合需要高精度伪造内容识别的研究与应用
深度伪造技术的泛滥对数字媒体真实性构成严峻挑战，亟需可靠的检测机制。本研究基于SP Cup 2025深度伪造检测挑战赛数据集，评估多种深度学习模型在深度伪造内容检测中的表现。通过结合传统深度学习方法与新型网络架构，我们训练了一系列模型，并采用准确率等指标进行严格性能评估，验证了所提方法在复杂伪造样本上的有效性。
原文摘要 · Abstract (English)
The proliferation of deep fake technology poses significant challenges to digital media authenticity, necessitating robust detection mechanisms. This project evaluates deep fake detection using the SP Cup's 2025 deep fake detection challenge dataset. We focused on exploring various deep learning models for detecting deep fake content, utilizing traditional deep learning techniques alongside newer architectures. Our approach involved training a series of models and rigorously assessing their performance using metrics such as accuracy.
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