The ability to detect manipulation in multimedia data is vital in digital forensics. Existing Image Manipulation Detection (IMD) methods are mainly based on detecting anomalous features arisen from image editing or double compression artifacts. All existing IMD techniques encounter challenges when it comes to detecting small tampered regions from a large image. Moreover, compression-based IMD approaches face difficulties in cases of double compression of identical quality factors. To investigate the State-of-The-Art (SoTA) IMD methods in those challenging conditions, we introduce a new Challenging Image Manipulation Detection (CIMD) benchmark dataset, which consists of two subsets, for evaluating editing-based and compression-based IMD methods, respectively. The dataset images were manually taken and tampered with high-quality annotations. In addition, we propose a new two-branch network model based on HRNet that can better detect both the image-editing and compression artifacts in those challenging conditions. Extensive experiments on the CIMD benchmark show that our model significantly outperforms SoTA IMD methods on CIMD.

翻译：在数字取证领域，检测多媒体数据中的篡改能力至关重要。现有图像篡改检测方法主要依赖于检测图像编辑或双重压缩伪影产生的异常特征。所有现有图像篡改检测技术在检测大尺寸图像中的微小篡改区域时均面临挑战。此外，基于压缩的篡改检测方法在处理相同质量因子的双重压缩场景时存在困难。为探究当前最优图像篡改检测方法在上述挑战条件下的表现，我们提出了一个新的挑战性图像篡改检测基准数据集，该数据集包含两个子集，分别用于评估基于编辑和基于压缩的篡改检测方法。数据集图像均为人工拍摄并经过高质量标注的篡改处理。同时，我们提出了一种基于HRNet的新型双分支网络模型，该模型能在这些挑战条件下更有效地检测图像编辑和压缩伪影。在CIMD基准上的大量实验表明，我们的模型在CIMD上显著优于当前最优的篡改检测方法。