Manipulation tools that realistically edit images are widely available, making it easy for anyone to create and spread misinformation. In an attempt to fight fake news, forgery detection and localization methods were designed. However, existing methods struggle to accurately reveal manipulations found in images on the internet, i.e., in the wild. That is because the type of forgery is typically unknown, in addition to the tampering traces being damaged by recompression. This paper presents Comprint, a novel forgery detection and localization method based on the compression fingerprint or comprint. It is trained on pristine data only, providing generalization to detect different types of manipulation. Additionally, we propose a fusion of Comprint with the state-of-the-art Noiseprint, which utilizes a complementary camera model fingerprint. We carry out an extensive experimental analysis and demonstrate that Comprint has a high level of accuracy on five evaluation datasets that represent a wide range of manipulation types, mimicking in-the-wild circumstances. Most notably, the proposed fusion significantly outperforms state-of-the-art reference methods. As such, Comprint and the fusion Comprint+Noiseprint represent a promising forensics tool to analyze in-the-wild tampered images.
翻译:现实地编辑图像的操作工具非常广泛,使任何人都容易创建和传播错误信息。为了打击假新闻、伪造检测和本地化方法,设计了各种现有方法,以准确地披露互联网(即野外)图像中发现的操纵。这是因为伪造类型通常不为人知,除了再压缩损坏的篡改痕迹之外,伪造类型通常也不为人知。本文展示了基于压缩指纹或刻印或刻印的Comprint、一种新型伪造检测和本地化方法。它仅接受纯净数据培训,为检测不同类型的操纵提供一般化。此外,我们提议将Comprint与最先进的Niseasprint相融合,利用一个补充的相机模型指纹。我们进行了广泛的实验分析,并表明Comprint在五套评价数据集上具有高度的准确性,代表着广泛的操纵类型,在模糊环境中进行模拟。最显著的是,拟议的聚合大大超出最新参考方法,为检测不同类型操纵提供通用。此外,我们提议将Comprint与最先进的缩略图集,作为分析工具的缩图集。