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

A New Benchmark and Model for Challenging Image Manipulation Detection