Deep models have been widely and successfully used in image manipulation detection, which aims to classify tampered images and localize tampered regions. Most existing methods mainly focus on extracting global features from tampered images, while neglecting the relationships of local features between tampered and authentic regions within a single tampered image. To exploit such spatial relationships, we propose Proposal Contrastive Learning (PCL) for effective image manipulation detection. Our PCL consists of a two-stream architecture by extracting two types of global features from RGB and noise views respectively. To further improve the discriminative power, we exploit the relationships of local features through a proxy proposal contrastive learning task by attracting/repelling proposal-based positive/negative sample pairs. Moreover, we show that our PCL can be easily adapted to unlabeled data in practice, which can reduce manual labeling costs and promote more generalizable features. Extensive experiments among several standard datasets demonstrate that our PCL can be a general module to obtain consistent improvement. The code is available at https://github.com/Sandy-Zeng/PCL.

翻译：深度模型已广泛应用于图像篡改检测任务，该任务旨在对篡改图像进行分类并定位篡改区域。现有方法主要关注从篡改图像中提取全局特征，却忽略了单张篡改图像中篡改区域与真实区域之间局部特征的关系。为充分利用这种空间关系，我们提出了一种基于候选区域对比学习（PCL）的有效图像篡改检测方法。该方法采用双流架构，分别从RGB视图与噪声视图中提取两类全局特征。为了进一步提升判别能力，我们通过代理候选区域对比学习任务来挖掘局部特征关系——该方法通过吸引/排斥基于候选区域的正/负样本对实现。此外，我们证明了所提PCL方法在实践应用中可轻松适配无标签数据，从而降低人工标注成本并促进更通用的特征学习。在多个标准数据集上的大量实验表明，PCL可作为通用模块实现性能的一致提升。相关代码已开源至https://github.com/Sandy-Zeng/PCL。