Dense Feature Interaction Network for Image Inpainting Localization

• URL: http://arxiv.org/abs/2408.02191v1
• Date: Mon, 5 Aug 2024 02:35:13 GMT
• Title: Dense Feature Interaction Network for Image Inpainting Localization
• Authors: Ye Yao, Tingfeng Han, Shan Jia, Siwei Lyu,
• Abstract summary: Inpainting can be used to conceal or alter image contents in malicious manipulation of images. Existing methods mostly rely on a basic encoder-decoder structure, which often results in a high number of false positives. In this paper, we describe a new method for inpainting detection based on a Dense Feature Interaction Network (DeFI-Net)
• Score: 28.028361409524457
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Image inpainting, which is the task of filling in missing areas in an image, is a common image editing technique. Inpainting can be used to conceal or alter image contents in malicious manipulation of images, driving the need for research in image inpainting detection. Existing methods mostly rely on a basic encoder-decoder structure, which often results in a high number of false positives or misses the inpainted regions, especially when dealing with targets of varying semantics and scales. Additionally, the absence of an effective approach to capture boundary artifacts leads to less accurate edge localization. In this paper, we describe a new method for inpainting detection based on a Dense Feature Interaction Network (DeFI-Net). DeFI-Net uses a novel feature pyramid architecture to capture and amplify multi-scale representations across various stages, thereby improving the detection of image inpainting by better revealing feature-level interactions. Additionally, the network can adaptively direct the lower-level features, which carry edge and shape information, to refine the localization of manipulated regions while integrating the higher-level semantic features. Using DeFI-Net, we develop a method combining complementary representations to accurately identify inpainted areas. Evaluation on five image inpainting datasets demonstrate the effectiveness of our approach, which achieves state-of-the-art performance in detecting inpainting across diverse models.

Related papers

• Perceptual Artifacts Localization for Inpainting [60.5659086595901]
  We propose a new learning task of automatic segmentation of inpainting perceptual artifacts. We train advanced segmentation networks on a dataset to reliably localize inpainting artifacts within inpainted images. We also propose a new evaluation metric called Perceptual Artifact Ratio (PAR), which is the ratio of objectionable inpainted regions to the entire inpainted area.
  arXiv  Detail & Related papers  (2022-08-05T18:50:51Z)
• Cylin-Painting: Seamless {360\textdegree} Panoramic Image Outpainting and Beyond [136.18504104345453]
  We present a Cylin-Painting framework that involves meaningful collaborations between inpainting and outpainting. The proposed algorithm can be effectively extended to other panoramic vision tasks, such as object detection, depth estimation, and image super-resolution.
  arXiv  Detail & Related papers  (2022-04-18T21:18:49Z)
• ObjectFormer for Image Manipulation Detection and Localization [118.89882740099137]
  We propose ObjectFormer to detect and localize image manipulations. We extract high-frequency features of the images and combine them with RGB features as multimodal patch embeddings. We conduct extensive experiments on various datasets and the results verify the effectiveness of the proposed method.
  arXiv  Detail & Related papers  (2022-03-28T12:27:34Z)
• Adaptive Image Inpainting [43.02281823557039]
  Inpainting methods have shown significant improvements by using deep neural networks. The problem is rooted in the encoder layers' ineffectiveness in building a complete and faithful embedding of the missing regions. We propose a distillation based approach for inpainting, where we provide direct feature level supervision for the encoder layers.
  arXiv  Detail & Related papers  (2022-01-01T12:16:01Z)
• Restore from Restored: Single-image Inpainting [9.699531255678856]
  We present a novel and efficient self-supervised fine-tuning algorithm for inpainting networks. We update the parameters of the pre-trained inpainting networks by utilizing existing self-similar patches. We achieve state-of-the-art inpainting results on publicly available benchmark datasets.
  arXiv  Detail & Related papers  (2021-10-25T11:38:51Z)
• 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)
• Noise Doesn't Lie: Towards Universal Detection of Deep Inpainting [42.189768203036394]
  We make the first attempt towards universal detection of deep inpainting, where the detection network can generalize well. Our approach outperforms existing detection methods by a large margin and generalizes well to unseen deep inpainting techniques.
  arXiv  Detail & Related papers  (2021-06-03T01:29:29Z)
• Restore from Restored: Single-image Inpainting [9.699531255678856]
  We present a novel and efficient self-supervised fine-tuning algorithm for inpainting networks. We upgrade the parameters of the pretrained networks by utilizing existing self-similar patches within the given input image. We achieve state-of-the-art inpainting results on publicly available benchmark datasets.
  arXiv  Detail & Related papers  (2021-02-16T10:59:28Z)
• GIID-Net: Generalizable Image Inpainting Detection via Neural Architecture Search and Attention [19.599993572921065]
  malicious use of advanced image inpainting tools has led to increasing threats to the reliability of image data. To fight against the inpainting forgeries, we propose a novel end-to-end Generalizable Image Inpainting Detection Network (GIID-Net) The proposed GIID-Net consists of three sub-blocks: the enhancement block, the extraction block and the decision block.
  arXiv  Detail & Related papers  (2021-01-19T02:29:40Z)
• Free-Form Image Inpainting via Contrastive Attention Network [64.05544199212831]
  In image inpainting tasks, masks with any shapes can appear anywhere in images which form complex patterns. It is difficult for encoders to capture such powerful representations under this complex situation. We propose a self-supervised Siamese inference network to improve the robustness and generalization.
  arXiv  Detail & Related papers  (2020-10-29T14:46:05Z)
• Learning to Compose Hypercolumns for Visual Correspondence [57.93635236871264]
  We introduce a novel approach to visual correspondence that dynamically composes effective features by leveraging relevant layers conditioned on the images to match. The proposed method, dubbed Dynamic Hyperpixel Flow, learns to compose hypercolumn features on the fly by selecting a small number of relevant layers from a deep convolutional neural network.
  arXiv  Detail & Related papers  (2020-07-21T04:03:22Z)

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.