CDDB (Continual Deepfake Detection Benchmark)

Abstract: There have been emerging a number of benchmarks and techniques for the detection of deepfakes. However, very few works study the detection of incrementally appearing deepfakes in the real-world scenarios. To simulate the wild scenes, this paper suggests a continual deepfake detection benchmark (CDDB) over a new collection of deepfakes from both known and unknown generative models. The suggested CDDB designs multiple evaluations on the detection over easy, hard, and long sequence of deepfake tasks, with a set of appropriate measures. In addition, we exploit multiple approaches to adapt multiclass incremental learning methods, commonly used in the continual visual recognition, to the continual deepfake detection problem. We evaluate existing methods, including their adapted ones, on the proposed CDDB. Within the proposed benchmark, we explore some commonly known essentials of standard continual learning. Our study provides new insights on these essentials in the context of cont

摘要：随着深度伪造检测领域基准和技术的不断涌现，然而，关于现实场景中逐渐出现的深度伪造检测的研究却寥寥无几。为了模拟野外场景，本文提出了一种针对从已知及未知生成模型中收集的新一批深度伪造数据的持续深度伪造检测基准（CDDB）。所提出的CDDB在易于检测、困难检测以及长序列深度伪造任务上设计了多项评估，并配备了一系列适当的度量标准。此外，我们运用多种方法将连续视觉识别中常用的多类增量学习方法应用于持续深度伪造检测问题。我们对现有方法及其适应版本在所提出的CDDB上进行评估。在所提出的基准中，我们探讨了标准连续学习的一些众所周知的基本要素。本研究为这些基本要素在持续深度伪造检测的背景下提供了新的洞见。