A New Benchmark and Model for Challenging Image Manipulation Detection
This work addresses challenges in digital forensics for detecting image manipulations, but it is incremental as it builds on existing IMD methods with a new dataset and model.
The authors tackled the problem of detecting small tampered regions and double compression artifacts in image manipulation detection by introducing a new benchmark dataset (CIMD) and a two-branch network model based on HRNet, which significantly outperformed state-of-the-art methods on this benchmark.
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.