Image Manipulation Detection by Multi-View Multi-Scale Supervision

• URL: http://arxiv.org/abs/2104.06832v1
• Date: Wed, 14 Apr 2021 13:05:58 GMT
• Title: Image Manipulation Detection by Multi-View Multi-Scale Supervision
• Authors: Xinru Chen, Chengbo Dong, Jiaqi Ji, Juan Cao, Xirong Li
• Abstract summary: Key challenge of image manipulation detection is how to learn generalizable features that are sensitive to manipulations in novel data. In this paper we address both aspects by multi-view feature learning and multi-scale supervision. Our thoughts are realized by a new network which we term MVSS-Net.
• Score: 11.319080833880307
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The key challenge of image manipulation detection is how to learn generalizable features that are sensitive to manipulations in novel data, whilst specific to prevent false alarms on authentic images. Current research emphasizes the sensitivity, with the specificity overlooked. In this paper we address both aspects by multi-view feature learning and multi-scale supervision. By exploiting noise distribution and boundary artifact surrounding tampered regions, the former aims to learn semantic-agnostic and thus more generalizable features. The latter allows us to learn from authentic images which are nontrivial to taken into account by current semantic segmentation network based methods. Our thoughts are realized by a new network which we term MVSS-Net. Extensive experiments on five benchmark sets justify the viability of MVSS-Net for both pixel-level and image-level manipulation detection.

Related papers

• Towards Generic Image Manipulation Detection with Weakly-Supervised Self-Consistency Learning [49.43362803584032]
  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)
  arXiv  Detail & Related papers  (2023-09-03T19:19:56Z)
• MMNet: Multi-Collaboration and Multi-Supervision Network for Sequential Deepfake Detection [81.59191603867586]
  Sequential deepfake detection aims to identify forged facial regions with the correct sequence for recovery. The recovery of forged images requires knowledge of the manipulation model to implement inverse transformations. We propose Multi-Collaboration and Multi-Supervision Network (MMNet) that handles various spatial scales and sequential permutations in forged face images.
  arXiv  Detail & Related papers  (2023-07-06T02:32:08Z)
• Multi-spectral Class Center Network for Face Manipulation Detection and Localization [52.569170436393165]
  We propose a novel Multi-Spectral Class Center Network (MSCCNet) for face manipulation detection and localization. Based on the features of different frequency bands, the MSCC module collects multi-spectral class centers and computes pixel-to-class relations. Applying multi-spectral class-level representations suppresses the semantic information of the visual concepts which is insensitive to manipulated regions of forgery images.
  arXiv  Detail & Related papers  (2023-05-18T08:09:20Z)
• MSMG-Net: Multi-scale Multi-grained Supervised Metworks for Multi-task Image Manipulation Detection and Localization [1.14219428942199]
  A novel multi-scale multi-grained deep network (MSMG-Net) is proposed to automatically identify manipulated regions. In our MSMG-Net, a parallel multi-scale feature extraction structure is used to extract multi-scale features. The MSMG-Net can effectively perceive the object-level semantics and encode the edge artifact.
  arXiv  Detail & Related papers  (2022-11-06T14:58:21Z)
• Deep Convolutional Pooling Transformer for Deepfake Detection [54.10864860009834]
  We propose a deep convolutional Transformer to incorporate decisive image features both locally and globally. Specifically, we apply convolutional pooling and re-attention to enrich the extracted features and enhance efficacy. The proposed solution consistently outperforms several state-of-the-art baselines on both within- and cross-dataset experiments.
  arXiv  Detail & Related papers  (2022-09-12T15:05:41Z)
• MVSS-Net: Multi-View Multi-Scale Supervised Networks for Image Manipulation Detection [10.594107680952774]
  Key research question for image manipulation detection is how to learn generalizable features that are sensitive to manipulations in novel data. In this paper we address both aspects by multi-view feature learning and multi-scale supervision. Our thoughts are realized by a new network which we term MVSS-Net and its enhanced version MVSS-Net++.
  arXiv  Detail & Related papers  (2021-12-16T15:01:52Z)
• Detect and Locate: A Face Anti-Manipulation Approach with Semantic and Noise-level Supervision [67.73180660609844]
  We propose a conceptually simple but effective method to efficiently detect forged faces in an image. The proposed scheme relies on a segmentation map that delivers meaningful high-level semantic information clues about the image. The proposed model achieves state-of-the-art detection accuracy and remarkable localization performance.
  arXiv  Detail & Related papers  (2021-07-13T02:59:31Z)
• Self-supervised Multisensor Change Detection [14.191073951237772]
  We revisit multisensor analysis in context of self-supervised change detection in bi-temporal satellite images. Recent development of self-supervised learning methods has shown that some of them can even work with only few images. Motivated by this, in this work we propose a method for multi-sensor change detection using only the unlabeled target bi-temporal images.
  arXiv  Detail & Related papers  (2021-02-12T12:31:10Z)
• D-Unet: A Dual-encoder U-Net for Image Splicing Forgery Detection and Localization [108.8592577019391]
  Image splicing forgery detection is a global binary classification task that distinguishes the tampered and non-tampered regions by image fingerprints. We propose a novel network called dual-encoder U-Net (D-Unet) for image splicing forgery detection, which employs an unfixed encoder and a fixed encoder. In an experimental comparison study of D-Unet and state-of-the-art methods, D-Unet outperformed the other methods in image-level and pixel-level detection.
  arXiv  Detail & Related papers  (2020-12-03T10:54:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.