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：数字取证中，检测多媒体数据篡改的能力至关重要。现有图像篡改检测方法主要基于检测由图像编辑或双重压缩伪影引起的异常特征。所有现有图像篡改检测技术在处理从大图中检测微小篡改区域时均面临挑战。此外，基于压缩的图像篡改检测方法在处理相同质量因子的双重压缩情形时存在困难。为探究现有最先进图像篡改检测方法在这些挑战条件下的表现，我们引入了一个新的挑战性图像篡改检测基准数据集，包含两个子集，分别用于评估基于编辑和基于压缩的图像篡改检测方法。该数据集图像均为人工拍摄并经过高质量标注的篡改处理。此外，我们提出了一种基于HRNet的新型双分支网络模型，能够在这些挑战条件下更好地检测图像编辑和压缩伪影。在挑战性图像篡改检测基准上的大量实验表明，我们的模型在挑战性图像篡改检测中显著优于现有最先进图像篡改检测方法。