DeepMark: A Scalable and Robust Framework for DeepFake Video Detection

Abstract

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.