Towards Generic Image Manipulation Detection with Weakly-Supervised Self-Consistency Learning

• URL: http://arxiv.org/abs/2309.01246v1
• Date: Sun, 3 Sep 2023 19:19:56 GMT
• Title: Towards Generic Image Manipulation Detection with Weakly-Supervised Self-Consistency Learning
• Authors: Yuanhao Zhai, Tianyu Luan, David Doermann, Junsong Yuan
• Abstract summary: We propose weakly-supervised image manipulation detection. Such a setting can leverage more training images and has the potential to adapt quickly to new manipulation techniques. Two consistency properties are learned: multi-source consistency (MSC) and inter-patch consistency (IPC)
• Score: 49.43362803584032
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: As advanced image manipulation techniques emerge, detecting the manipulation becomes increasingly important. Despite the success of recent learning-based approaches for image manipulation detection, they typically require expensive pixel-level annotations to train, while exhibiting degraded performance when testing on images that are differently manipulated compared with training images. To address these limitations, we propose weakly-supervised image manipulation detection, such that only binary image-level labels (authentic or tampered with) are required for training purpose. Such a weakly-supervised setting can leverage more training images and has the potential to adapt quickly to new manipulation techniques. To improve the generalization ability, we propose weakly-supervised self-consistency learning (WSCL) to leverage the weakly annotated images. Specifically, two consistency properties are learned: multi-source consistency (MSC) and inter-patch consistency (IPC). MSC exploits different content-agnostic information and enables cross-source learning via an online pseudo label generation and refinement process. IPC performs global pair-wise patch-patch relationship reasoning to discover a complete region of manipulation. Extensive experiments validate that our WSCL, even though is weakly supervised, exhibits competitive performance compared with fully-supervised counterpart under both in-distribution and out-of-distribution evaluations, as well as reasonable manipulation localization ability.

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