Papers › ObjectFormer for Image Manipulation Detection and Localization

ObjectFormer for Image Manipulation Detection and Localization

28 Mar 2022CVPR 2022 1arXiv:2203.14681archive 2025-07-28

Junke Wang, Zuxuan Wu, Jingjing Chen, Xintong Han, Abhinav Shrivastava, Ser-Nam Lim, Yu-Gang Jiang

Recent advances in image editing techniques have posed serious challenges to the trustworthiness of multimedia data, which drives the research of image tampering detection. In this paper, we propose ObjectFormer to detect and localize image manipulations. To capture subtle manipulation traces that are no longer visible in the RGB domain, we extract high-frequency features of the images and combine them with RGB features as multimodal patch embeddings. Additionally, we use a set of learnable object prototypes as mid-level representations to model the object-level consistencies among different regions, which are further used to refine patch embeddings to capture the patch-level consistencies. We conduct extensive experiments on various datasets and the results verify the effectiveness of the proposed method, outperforming state-of-the-art tampering detection and localization methods.

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Tasks

Image ManipulationImage Manipulation DetectionImage Manipulation Localization

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Image Manipulation Localization CASIAv1(Protoclo-CAT) ObjectFormer Pixel Binary F1 0.531 #7 of 8 Archive leaderboard report
Image Manipulation Localization COVERAGE(Protocol-CAT) ObjectFormer Pixel Binary F1 0.257 #7 of 8 Archive leaderboard report
Image Manipulation Localization Columbia(Protocol-CAT) ObjectFormer Pixel Binary F1 0.732 #7 of 8 Archive leaderboard report
Image Manipulation Localization NIST16(Protocol-CAT) ObjectFormer Pixel Binary F1 0.252 #7 of 8 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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