This work explores various ways of exploring multi-task learning (MTL) techniques aimed at classifying videos as original or manipulated in cross-manipulation scenario to attend generalizability in deep fake scenario. The dataset used in our evaluation is FaceForensics++, which features 1000 original videos manipulated by four different techniques, with a total of 5000 videos. We conduct extensive experiments on multi-task learning and contrastive techniques, which are well studied in literature for their generalization benefits. It can be concluded that the proposed detection model is quite generalized, i.e., accurately detects manipulation methods not encountered during training as compared to the state-of-the-art.

翻译：本研究探索了多种多任务学习（MTL）技术，旨在跨操作场景下对视频进行分类（原始或篡改），以提升深度伪造场景中的泛化能力。实验所用数据集为FaceForensics++，包含1000个原始视频及采用四种不同技术篡改的对应版本，共计5000个视频。我们针对多任务学习及对比技术开展了广泛实验，这些技术在文献中因其泛化优势而受到充分研究。结果表明，所提出的检测模型具有高度泛化性，即与现有最优方法相比，该模型能准确检测训练中未见的篡改方法。