With the rapid growth of DeepFake video techniques, it becomes increasingly challenging to identify them visually, posing a huge threat to our society. Unfortunately, existing detection schemes are limited to exploiting the artifacts left by DeepFake manipulations, so they struggle to keep pace with the ever-improving DeepFake models. In this work, we propose DeepMark, a scalable and robust framework for detecting DeepFakes. It imprints essential visual features of a video into DeepMark Meta (DMM) and uses it to detect DeepFake manipulations by comparing the extracted visual features with the ground truth in DMM. Therefore, DeepMark is future-proof, because a DeepFake video must aim to alter some visual feature, no matter how “natural” it looks. Furthermore, DMM also contains a signature for verifying the integrity of the above features. And an essential link to the features as well as their signature is attached with error correction codes and embedded in the video watermark. To improve the efficiency of DMM creation, we also present a threshold-based feature selection scheme and a deduced face detection scheme. Experimental results demonstrate the effectiveness and efficiency of DeepMark on DeepFake video detection under various datasets and parameter settings.

随着 DeepFake 视频技术的快速发展，视觉识别它们变得越来越困难，对我们的社会构成了巨大的威胁。不幸的是，现有的检测方案仅限于利用 DeepFake 操作留下的痕迹，因此它们很难跟上不断改进的 DeepFake 模型的步伐。在这项工作中，我们提出了 DeepMark，一种用于检测 DeepFakes 的可扩展且强大的框架。它将视频的基本视觉特征印入 DeepMark Meta (DMM) 中，并通过将提取的视觉特征与 DMM 中的基本事实进行比较来使用它来检测 DeepFake 操作。因此，DeepMark 是面向未来的，因为 DeepFake 视频必须旨在改变某些视觉特征，无论它看起来多么“自然”。此外，DMM还包含用于验证上述特征完整性的签名。功能及其签名的重要链接附有纠错代码并嵌入视频水印中。为了提高 DMM 创建的效率，我们还提出了一种基于阈值的特征选择方案和推导的人脸检测方案。实验结果证明了 DeepMark 在各种数据集和参数设置下对 DeepFake 视频检测的有效性和效率。