Towards Generalized Physical Occlusion Detection On Documents

Fake document detection is an important area in image forensics. Most of the existing techniques focus on the detection of digitally forged documents. In this paper, we look into the forgery of generalized physical occlusion, which is a simple and effective strategy to generate fake document images. We propose an Adversary Decomposition Network (ADDNet) to effectively extract generalized physical occlusion features from various types of documents, where two adversarial classifiers are designed and trained for feature decomposition. On top of the ADDNet, we further propose a lightweight Document Adapter (DA) for flexible and scalable fake document detection, which works well when we encounter a new type of document with limited samples for fine-tuning. To facilitate the research, we newly construct a dataset for physical occlusion detection on different types of documents. Various experiments are carried out to demonstrate the advantage of our proposed scheme over the existing schemes for physical occlusion detection, especially when the document is unseen or has limited samples in training.